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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
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stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2210.16998
|
Ali Ebnenasir
|
Ebrahim Fazli and Ali Ebnenasir
|
TPGen: A Self-Stabilizing GPU-Based Method for Prime and Test Paths
Generation
| null | null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents a novel scalable GPU-based method for Test Paths (TPs)
and Prime Paths (PPs) Generation, called TPGen, used in structural testing and
in test data generation. TPGen outperforms existing methods for PPs and TPs
generation in several orders of magnitude, both in time and space efficiency.
Improving both time and space efficiency is made possible through devising a
new non-contiguous and hierarchical memory allocation method, called
Three-level Path Access Method (TPAM), that enables efficient storage of
maximal simple paths in memory. In addition to its high time and space
efficiency, a major significance of TPGen includes its self-stabilizing design
where threads execute in a fully asynchronous and order-oblivious way without
using any atomic instructions. TPGen can generate PPs and TPs of structurally
complex programs that have an extremely high cyclomatic and Npath complexity.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 00:55:01 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Fazli",
"Ebrahim",
""
],
[
"Ebnenasir",
"Ali",
""
]
] |
new_dataset
| 0.971878 |
2210.17008
|
Robin Hankin Dr
|
Robin K. S. Hankin
|
Stokes's theorem in R
|
18 pages
| null | null | null |
cs.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this short article I introduce the stokes package which provides
functionality for working with tensors, alternating forms, wedge products, and
related concepts from the exterior calculus. Notation and spirit follow Spivak.
Stokes's generalized integral theorem, viz $\int_{\partial X}\phi=\int_Xd\phi$,
is demonstrated here using the package; it is available on CRAN
athttps://CRAN.R-project.org/package=stokes.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 01:51:36 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Hankin",
"Robin K. S.",
""
]
] |
new_dataset
| 0.999514 |
2210.17057
|
Lei Kou
|
Lei Kou, Chuang Liu, Guo-wei Cai, Jia-ning Zhou, Quan-de Yuan, Si-miao
Pang
|
Fault diagnosis for open-circuit faults in NPC inverter based on
knowledge-driven and data-driven approaches
|
IET Power Electronics
| null |
10.1049/iet-pel.2019.0835
| null |
cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this study, the open-circuit faults diagnosis and location issue of the
neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis
approach based on knowledge driven and data driven was presented for the
open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC
inverter, and Concordia transform (knowledge driven) and random forests (RFs)
technique (data driven) are employed to improve the robustness performance of
the fault diagnosis classifier. First, the fault feature data of AC in either
normal state or open-circuit faults states of NPC inverter are analysed and
extracted. Second, the Concordia transform is used to process the fault
samples, and it has been verified that the slopes of current trajectories are
not affected by different loads in this study, which can help the proposed
method to reduce overdependence on fault data. Moreover, then the transformed
fault samples are adopted to train the RFs fault diagnosis classifier, and the
fault diagnosis results show that the classification accuracy and robustness
performance of the fault diagnosis classifier are improved. Finally, the
diagnosis results of online fault diagnosis experiments show that the proposed
classifier can locate the open-circuit fault of IGBTs in NPC inverter under the
conditions of different loads.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 04:33:53 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Kou",
"Lei",
""
],
[
"Liu",
"Chuang",
""
],
[
"Cai",
"Guo-wei",
""
],
[
"Zhou",
"Jia-ning",
""
],
[
"Yuan",
"Quan-de",
""
],
[
"Pang",
"Si-miao",
""
]
] |
new_dataset
| 0.999559 |
2210.17086
|
Gali Sheffi
|
Gali Sheffi, Pedro Ramalhete and Erez Petrank
|
EEMARQ: Efficient Lock-Free Range Queries with Memory Reclamation
| null | null | null | null |
cs.DB cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Multi-Version Concurrency Control (MVCC) is a common mechanism for achieving
linearizable range queries in database systems and concurrent data-structures.
The core idea is to keep previous versions of nodes to serve range queries,
while still providing atomic reads and updates. Existing concurrent
data-structure implementations, that support linearizable range queries, are
either slow, use locks, or rely on blocking reclamation schemes. We present
EEMARQ, the first scheme that uses MVCC with lock-free memory reclamation to
obtain a fully lock-free data-structure supporting linearizable inserts,
deletes, contains, and range queries. Evaluation shows that EEMARQ outperforms
existing solutions across most workloads, with lower space overhead and while
providing full lock freedom.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 06:23:05 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Sheffi",
"Gali",
""
],
[
"Ramalhete",
"Pedro",
""
],
[
"Petrank",
"Erez",
""
]
] |
new_dataset
| 0.95626 |
2210.17115
|
Zhenzhe Hechen
|
Zhenzhe Hechen, Wei Huang, Yixin Zhao
|
ViT-LSLA: Vision Transformer with Light Self-Limited-Attention
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transformers have demonstrated a competitive performance across a wide range
of vision tasks, while it is very expensive to compute the global
self-attention. Many methods limit the range of attention within a local window
to reduce computation complexity. However, their approaches cannot save the
number of parameters; meanwhile, the self-attention and inner position bias
(inside the softmax function) cause each query to focus on similar and close
patches. Consequently, this paper presents a light self-limited-attention
(LSLA) consisting of a light self-attention mechanism (LSA) to save the
computation cost and the number of parameters, and a self-limited-attention
mechanism (SLA) to improve the performance. Firstly, the LSA replaces the K
(Key) and V (Value) of self-attention with the X(origin input). Applying it in
vision Transformers which have encoder architecture and self-attention
mechanism, can simplify the computation. Secondly, the SLA has a positional
information module and a limited-attention module. The former contains a
dynamic scale and an inner position bias to adjust the distribution of the
self-attention scores and enhance the positional information. The latter uses
an outer position bias after the softmax function to limit some large values of
attention weights. Finally, a hierarchical Vision Transformer with Light
self-Limited-attention (ViT-LSLA) is presented. The experiments show that
ViT-LSLA achieves 71.6% top-1 accuracy on IP102 (2.4% absolute improvement of
Swin-T); 87.2% top-1 accuracy on Mini-ImageNet (3.7% absolute improvement of
Swin-T). Furthermore, it greatly reduces FLOPs (3.5GFLOPs vs. 4.5GFLOPs of
Swin-T) and parameters (18.9M vs. 27.6M of Swin-T).
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 07:46:45 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Hechen",
"Zhenzhe",
""
],
[
"Huang",
"Wei",
""
],
[
"Zhao",
"Yixin",
""
]
] |
new_dataset
| 0.994322 |
2210.17130
|
Kohei Suenaga
|
Atsushi Kikuchi, Kotaro Uchida, Masaki Waga, Kohei Suenaga
|
BOREx: Bayesian-Optimization--Based Refinement of Saliency Map for
Image- and Video-Classification Models
|
32 pages. To appear in ACCV 2022
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Explaining a classification result produced by an image- and
video-classification model is one of the important but challenging issues in
computer vision. Many methods have been proposed for producing heat-map--based
explanations for this purpose, including ones based on the white-box approach
that uses the internal information of a model (e.g., LRP, Grad-CAM, and
Grad-CAM++) and ones based on the black-box approach that does not use any
internal information (e.g., LIME, SHAP, and RISE). We propose a new black-box
method BOREx (Bayesian Optimization for Refinement of visual model Explanation)
to refine a heat map produced by any method. Our observation is that a
heat-map--based explanation can be seen as a prior for an explanation method
based on Bayesian optimization. Based on this observation, BOREx conducts
Gaussian process regression (GPR) to estimate the saliency of each pixel in a
given image starting from the one produced by another explanation method. Our
experiments statistically demonstrate that the refinement by BOREx improves
low-quality heat maps for image- and video-classification results.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 08:25:12 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Kikuchi",
"Atsushi",
""
],
[
"Uchida",
"Kotaro",
""
],
[
"Waga",
"Masaki",
""
],
[
"Suenaga",
"Kohei",
""
]
] |
new_dataset
| 0.995514 |
2210.17151
|
Deokki Hong
|
Deokki Hong
|
Tech Report: One-stage Lightweight Object Detectors
| null | null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
This work is for designing one-stage lightweight detectors which perform well
in terms of mAP and latency. With baseline models each of which targets on GPU
and CPU respectively, various operations are applied instead of the main
operations in backbone networks of baseline models. In addition to experiments
about backbone networks and operations, several feature pyramid network (FPN)
architectures are investigated. Benchmarks and proposed detectors are analyzed
in terms of the number of parameters, Gflops, GPU latency, CPU latency and mAP,
on MS COCO dataset which is a benchmark dataset in object detection. This work
propose similar or better network architectures considering the trade-off
between accuracy and latency. For example, our proposed GPU-target backbone
network outperforms that of YOLOX-tiny which is selected as the benchmark by
1.43x in speed and 0.5 mAP in accuracy on NVIDIA GeForce RTX 2080 Ti GPU.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 09:02:37 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Hong",
"Deokki",
""
]
] |
new_dataset
| 0.998306 |
2210.17185
|
Ayush Tripathi
|
Ayush Tripathi, Lalan Kumar, Prathosh A.P., Suriya Prakash
Muthukrishnan
|
SurfMyoAiR: A surface Electromyography based framework for Airwriting
Recognition
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Airwriting Recognition is the task of identifying letters written in free
space with finger movement. Electromyography (EMG) is a technique used to
record electrical activity during muscle contraction and relaxation as a result
of movement and is widely used for gesture recognition. Most of the current
research in gesture recognition is focused on identifying static gestures.
However, dynamic gestures are natural and user-friendly for being used as
alternate input methods in Human-Computer Interaction applications. Airwriting
recognition using EMG signals recorded from forearm muscles is therefore a
viable solution. Since the user does not need to learn any new gestures and a
large range of words can be formed by concatenating these letters, it is
generalizable to a wider population. There has been limited work in recognition
of airwriting using EMG signals and forms the core idea of the current work.
The SurfMyoAiR dataset comprising of EMG signals recorded during writing
English uppercase alphabets is constructed. Several different time-domain
features to construct EMG envelope and two different time-frequency image
representations: Short-Time Fourier Transform and Continuous Wavelet Transform
were explored to form the input to a deep learning model for airwriting
recognition. Several different deep learning architectures were exploited for
this task. Additionally, the effect of various parameters such as signal
length, window length and interpolation techniques on the recognition
performance is comprehensively explored. The best-achieved accuracy was 78.50%
and 62.19% in user-dependent and independent scenarios respectively by using
Short-Time Fourier Transform in conjunction with a 2D Convolutional Neural
Network based classifier. Airwriting has great potential as a user-friendly
modality to be used as an alternate input method in Human-Computer Interaction
applications.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 10:08:34 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Tripathi",
"Ayush",
""
],
[
"Kumar",
"Lalan",
""
],
[
"P.",
"Prathosh A.",
""
],
[
"Muthukrishnan",
"Suriya Prakash",
""
]
] |
new_dataset
| 0.999649 |
2210.17190
|
Shubham Mittal
|
Shubham Mittal and Preslav Nakov
|
IITD at the WANLP 2022 Shared Task: Multilingual Multi-Granularity
Network for Propaganda Detection
| null | null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We present our system for the two subtasks of the shared task on propaganda
detection in Arabic, part of WANLP'2022. Subtask 1 is a multi-label
classification problem to find the propaganda techniques used in a given tweet.
Our system for this task uses XLM-R to predict probabilities for the target
tweet to use each of the techniques. In addition to finding the techniques,
Subtask 2 further asks to identify the textual span for each instance of each
technique that is present in the tweet; the task can be modeled as a sequence
tagging problem. We use a multi-granularity network with mBERT encoder for
Subtask 2. Overall, our system ranks second for both subtasks (out of 14 and 3
participants, respectively). Our empirical analysis show that it does not help
to use a much larger English corpus annotated with propaganda techniques,
regardless of whether used in English or after translation to Arabic.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 10:14:43 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Mittal",
"Shubham",
""
],
[
"Nakov",
"Preslav",
""
]
] |
new_dataset
| 0.986257 |
2210.17236
|
Daoguang Zan
|
Daoguang Zan, Bei Chen, Zeqi Lin, Bei Guan, Yongji Wang, Jian-Guang
Lou
|
When Language Model Meets Private Library
|
EMNLP 2022 Findings
| null | null | null |
cs.PL cs.CL cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
With the rapid development of pre-training techniques, a number of language
models have been pre-trained on large-scale code corpora and perform well in
code generation. In this paper, we investigate how to equip pre-trained
language models with the ability of code generation for private libraries. In
practice, it is common for programmers to write code using private libraries.
However, this is a challenge for language models since they have never seen
private APIs during training. Motivated by the fact that private libraries
usually come with elaborate API documentation, we propose a novel framework
with two modules: the APIRetriever finds useful APIs, and then the APICoder
generates code using these APIs. For APIRetriever, we present a dense retrieval
system and also design a friendly interaction to involve uses. For APICoder, we
can directly use off-the-shelf language models, or continually pre-train the
base model on a code corpus containing API information. Both modules are
trained with data from public libraries and can be generalized to private ones.
Furthermore, we craft three benchmarks for private libraries, named
TorchDataEval, MonkeyEval, and BeatNumEval. Experimental results demonstrate
the impressive performance of our framework.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 11:42:06 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Zan",
"Daoguang",
""
],
[
"Chen",
"Bei",
""
],
[
"Lin",
"Zeqi",
""
],
[
"Guan",
"Bei",
""
],
[
"Wang",
"Yongji",
""
],
[
"Lou",
"Jian-Guang",
""
]
] |
new_dataset
| 0.951138 |
2210.17414
|
Sanjay Adhikesaven
|
Sanjay Adhikesaven
|
An Industrial Workplace Alerting and Monitoring Platform to Prevent
Workplace Injury and Accidents
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Workplace accidents are a critical problem that causes many deaths, injuries,
and financial losses. Climate change has a severe impact on industrial workers,
partially caused by global warming. To reduce such casualties, it is important
to proactively find unsafe environments where injuries could occur by detecting
the use of personal protective equipment (PPE) and identifying unsafe
activities. Thus, we propose an industrial workplace alerting and monitoring
platform to detect PPE use and classify unsafe activity in group settings
involving multiple humans and objects over a long period of time. Our proposed
method is the first to analyze prolonged actions involving multiple people or
objects. It benefits from combining pose estimation with PPE detection in one
platform. Additionally, we propose the first open source annotated data set
with video data from industrial workplaces annotated with the action
classifications and detected PPE. The proposed system can be implemented within
the surveillance cameras already present in industrial settings, making it a
practical and effective solution.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 06:35:00 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Adhikesaven",
"Sanjay",
""
]
] |
new_dataset
| 0.993637 |
2210.17491
|
Julian Whitman
|
Julian Whitman and Howie Choset
|
Learning Modular Robot Locomotion from Demonstrations
| null | null | null | null |
cs.RO cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Modular robots can be reconfigured to create a variety of designs from a
small set of components. But constructing a robot's hardware on its own is not
enough -- each robot needs a controller. One could create controllers for some
designs individually, but developing policies for additional designs can be
time consuming. This work presents a method that uses demonstrations from one
set of designs to accelerate policy learning for additional designs. We
leverage a learning framework in which a graph neural network is made up of
modular components, each component corresponds to a type of module (e.g., a
leg, wheel, or body) and these components can be recombined to learn from
multiple designs at once. In this paper we develop a combined reinforcement and
imitation learning algorithm. Our method is novel because the policy is
optimized to both maximize a reward for one design, and simultaneously imitate
demonstrations from different designs, within one objective function. We show
that when the modular policy is optimized with this combined objective,
demonstrations from one set of designs influence how the policy behaves on a
different design, decreasing the number of training iterations needed.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 17:15:32 GMT"
}
] | 2022-11-01T00:00:00 |
[
[
"Whitman",
"Julian",
""
],
[
"Choset",
"Howie",
""
]
] |
new_dataset
| 0.999127 |
2106.14651
|
Sam Kumar
|
Sam Kumar, David E. Culler, Raluca Ada Popa
|
MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation
|
19 pages; Accepted to OSDI 2021
| null | null | null |
cs.OS cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Secure Computation (SC) is a family of cryptographic primitives for computing
on encrypted data in single-party and multi-party settings. SC is being
increasingly adopted by industry for a variety of applications. A significant
obstacle to using SC for practical applications is the memory overhead of the
underlying cryptography. We develop MAGE, an execution engine for SC that
efficiently runs SC computations that do not fit in memory. We observe that,
due to their intended security guarantees, SC schemes are inherently oblivious
-- their memory access patterns are independent of the input data. Using this
property, MAGE calculates the memory access pattern ahead of time and uses it
to produce a memory management plan. This formulation of memory management,
which we call memory programming, is a generalization of paging that allows
MAGE to provide a highly efficient virtual memory abstraction for SC. MAGE
outperforms the OS virtual memory system by up to an order of magnitude, and in
many cases, runs SC computations that do not fit in memory at nearly the same
speed as if the underlying machines had unbounded physical memory to fit the
entire computation.
|
[
{
"version": "v1",
"created": "Wed, 23 Jun 2021 23:44:27 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 22:31:58 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Kumar",
"Sam",
""
],
[
"Culler",
"David E.",
""
],
[
"Popa",
"Raluca Ada",
""
]
] |
new_dataset
| 0.953342 |
2108.09372
|
Archana Patel
|
Archana Patel, Sarika Jain, Narayan C. Debnath, Vishal Lama
|
InBiodiv-O: An Ontology for Indian Biodiversity Knowledge Management
|
This paper has been withdrawn by the author due to many grammatical
errors, and inconsistent content
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
To present the biodiversity information, a semantic model is required that
connects all kinds of data about living creatures and their habitats. The model
must be able to encode human knowledge for machines to be understood. Ontology
offers the richest machine-interpretable (rather than just machine-processable)
and explicit semantics that are being extensively used in the biodiversity
domain. Various ontologies are developed for the biodiversity domain however a
review of the current landscape shows that these ontologies are not capable to
define the Indian biodiversity information though India is one of the
megadiverse countries. To semantically analyze the Indian biodiversity
information, it is crucial to build an ontology that describes all the
essential terms of this domain from the unstructured format of the data
available on the web. Since, the curation of the ontologies heavily depends on
the domain where these are implemented hence there is no ideal methodology is
defined yet to be ready for universal use. The aim of this article is to
develop an ontology that semantically encodes all the terms of Indian
biodiversity information in all its dimensions based on the proposed
methodology. The comprehensive evaluation of the proposed ontology depicts that
ontology is well built in the specified domain.
|
[
{
"version": "v1",
"created": "Fri, 20 Aug 2021 21:07:46 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 08:10:43 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Patel",
"Archana",
""
],
[
"Jain",
"Sarika",
""
],
[
"Debnath",
"Narayan C.",
""
],
[
"Lama",
"Vishal",
""
]
] |
new_dataset
| 0.999166 |
2110.08565
|
Domenico Tortorella
|
Domenico Tortorella, Alessio Micheli
|
Dynamic Graph Echo State Networks
|
Accepted for oral presentation at ESANN 2021
|
Proceedings of the 29th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN 2021), pp.
99-104
|
10.14428/esann/2021.ES2021-70
| null |
cs.LG cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dynamic temporal graphs represent evolving relations between entities, e.g.
interactions between social network users or infection spreading. We propose an
extension of graph echo state networks for the efficient processing of dynamic
temporal graphs, with a sufficient condition for their echo state property, and
an experimental analysis of reservoir layout impact. Compared to temporal graph
kernels that need to hold the entire history of vertex interactions, our model
provides a vector encoding for the dynamic graph that is updated at each
time-step without requiring training. Experiments show accuracy comparable to
approximate temporal graph kernels on twelve dissemination process
classification tasks.
|
[
{
"version": "v1",
"created": "Sat, 16 Oct 2021 12:51:50 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 19:39:01 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Tortorella",
"Domenico",
""
],
[
"Micheli",
"Alessio",
""
]
] |
new_dataset
| 0.993235 |
2202.09367
|
Peng Xiang
|
Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng,
Zhizhong Han
|
Snowflake Point Deconvolution for Point Cloud Completion and Generation
with Skip-Transformer
|
IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI), 2022. This work is a journal extension of our ICCV 2021 paper
arXiv:2108.04444 . The first two authors contributed equally
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most existing point cloud completion methods suffer from the discrete nature
of point clouds and the unstructured prediction of points in local regions,
which makes it difficult to reveal fine local geometric details. To resolve
this issue, we propose SnowflakeNet with snowflake point deconvolution (SPD) to
generate complete point clouds. SPD models the generation of point clouds as
the snowflake-like growth of points, where child points are generated
progressively by splitting their parent points after each SPD. Our insight into
the detailed geometry is to introduce a skip-transformer in the SPD to learn
the point splitting patterns that can best fit the local regions. The
skip-transformer leverages attention mechanism to summarize the splitting
patterns used in the previous SPD layer to produce the splitting in the current
layer. The locally compact and structured point clouds generated by SPD
precisely reveal the structural characteristics of the 3D shape in local
patches, which enables us to predict highly detailed geometries. Moreover,
since SPD is a general operation that is not limited to completion, we explore
its applications in other generative tasks, including point cloud
auto-encoding, generation, single image reconstruction, and upsampling. Our
experimental results outperform state-of-the-art methods under widely used
benchmarks.
|
[
{
"version": "v1",
"created": "Fri, 18 Feb 2022 17:09:49 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Feb 2022 11:58:29 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Oct 2022 06:36:31 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Xiang",
"Peng",
""
],
[
"Wen",
"Xin",
""
],
[
"Liu",
"Yu-Shen",
""
],
[
"Cao",
"Yan-Pei",
""
],
[
"Wan",
"Pengfei",
""
],
[
"Zheng",
"Wen",
""
],
[
"Han",
"Zhizhong",
""
]
] |
new_dataset
| 0.976121 |
2203.10885
|
Qiang Sheng
|
Qiang Sheng, Juan Cao, Xueyao Zhang, Rundong Li, Danding Wang,
Yongchun Zhu
|
Zoom Out and Observe: News Environment Perception for Fake News
Detection
|
ACL 2022 Main Conference (Long Paper)
| null |
10.18653/v1/2022.acl-long.311
| null |
cs.CL cs.CY cs.SI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Fake news detection is crucial for preventing the dissemination of
misinformation on social media. To differentiate fake news from real ones,
existing methods observe the language patterns of the news post and "zoom in"
to verify its content with knowledge sources or check its readers' replies.
However, these methods neglect the information in the external news environment
where a fake news post is created and disseminated. The news environment
represents recent mainstream media opinion and public attention, which is an
important inspiration of fake news fabrication because fake news is often
designed to ride the wave of popular events and catch public attention with
unexpected novel content for greater exposure and spread. To capture the
environmental signals of news posts, we "zoom out" to observe the news
environment and propose the News Environment Perception Framework (NEP). For
each post, we construct its macro and micro news environment from recent
mainstream news. Then we design a popularity-oriented and a novelty-oriented
module to perceive useful signals and further assist final prediction.
Experiments on our newly built datasets show that the NEP can efficiently
improve the performance of basic fake news detectors.
|
[
{
"version": "v1",
"created": "Mon, 21 Mar 2022 11:10:46 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 02:48:21 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Sheng",
"Qiang",
""
],
[
"Cao",
"Juan",
""
],
[
"Zhang",
"Xueyao",
""
],
[
"Li",
"Rundong",
""
],
[
"Wang",
"Danding",
""
],
[
"Zhu",
"Yongchun",
""
]
] |
new_dataset
| 0.99835 |
2205.09641
|
Tanya Goyal
|
Tanya Goyal, Junyi Jessy Li, Greg Durrett
|
SNaC: Coherence Error Detection for Narrative Summarization
|
EMNLP 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Progress in summarizing long texts is inhibited by the lack of appropriate
evaluation frameworks. When a long summary must be produced to appropriately
cover the facets of that text, that summary needs to present a coherent
narrative to be understandable by a reader, but current automatic and human
evaluation methods fail to identify gaps in coherence. In this work, we
introduce SNaC, a narrative coherence evaluation framework rooted in
fine-grained annotations for long summaries. We develop a taxonomy of coherence
errors in generated narrative summaries and collect span-level annotations for
6.6k sentences across 150 book and movie screenplay summaries. Our work
provides the first characterization of coherence errors generated by
state-of-the-art summarization models and a protocol for eliciting coherence
judgments from crowd annotators. Furthermore, we show that the collected
annotations allow us to train a strong classifier for automatically localizing
coherence errors in generated summaries as well as benchmarking past work in
coherence modeling. Finally, our SNaC framework can support future work in long
document summarization and coherence evaluation, including improved
summarization modeling and post-hoc summary correction.
|
[
{
"version": "v1",
"created": "Thu, 19 May 2022 16:01:47 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 15:28:59 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Goyal",
"Tanya",
""
],
[
"Li",
"Junyi Jessy",
""
],
[
"Durrett",
"Greg",
""
]
] |
new_dataset
| 0.989385 |
2205.12206
|
Aitor Ormazabal
|
Aitor Ormazabal, Mikel Artetxe, Manex Agirrezabal, Aitor Soroa and
Eneko Agirre
|
PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised
Poetry Generation
|
EMNLP Findings 2022
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Formal verse poetry imposes strict constraints on the meter and rhyme scheme
of poems. Most prior work on generating this type of poetry uses existing poems
for supervision, which are difficult to obtain for most languages and poetic
forms. In this work, we propose an unsupervised approach to generate poems
following any given meter and rhyme scheme, without requiring any poetic text
for training. Our method works by splitting a regular, non-poetic corpus into
phrases, prepending control codes that describe the length and end rhyme of
each phrase, and training a transformer language model in the augmented corpus.
During inference, we build control codes for the desired meter and rhyme
scheme, and condition our language model on them to generate formal verse
poetry. Experiments in Spanish and Basque show that our approach is able to
generate valid poems, which are often comparable in quality to those written by
humans.
|
[
{
"version": "v1",
"created": "Tue, 24 May 2022 17:09:55 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 11:57:12 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Ormazabal",
"Aitor",
""
],
[
"Artetxe",
"Mikel",
""
],
[
"Agirrezabal",
"Manex",
""
],
[
"Soroa",
"Aitor",
""
],
[
"Agirre",
"Eneko",
""
]
] |
new_dataset
| 0.998445 |
2206.14976
|
Nibraas Khan
|
Nibraas Khan, Nilanjan Sarkar
|
Semi-Supervised Generative Adversarial Network for Stress Detection
Using Partially Labeled Physiological Data
|
12 pages
| null | null | null |
cs.LG cs.AI eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Physiological measurements involves observing variables that attribute to the
normative functioning of human systems and subsystems directly or indirectly.
The measurements can be used to detect affective states of a person with aims
such as improving human-computer interactions. There are several methods of
collecting physiological data, but wearable sensors are a common, non-invasive
tool for accurate readings. However, valuable information is hard to extract
from the raw physiological data, especially for affective state detection.
Machine Learning techniques are used to detect the affective state of a person
through labeled physiological data. A clear problem with using labeled data is
creating accurate labels. An expert is needed to analyze a form of recording of
participants and mark sections with different states such as stress and calm.
While expensive, this method delivers a complete dataset with labeled data that
can be used in any number of supervised algorithms. An interesting question
arises from the expensive labeling: how can we reduce the cost while
maintaining high accuracy? Semi-Supervised learning (SSL) is a potential
solution to this problem. These algorithms allow for machine learning models to
be trained with only a small subset of labeled data (unlike unsupervised which
use no labels). They provide a way of avoiding expensive labeling. This paper
compares a fully supervised algorithm to a SSL on the public WESAD (Wearable
Stress and Affect Detection) Dataset for stress detection. This paper shows
that Semi-Supervised algorithms are a viable method for inexpensive affective
state detection systems with accurate results.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 01:58:33 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 19:47:23 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Khan",
"Nibraas",
""
],
[
"Sarkar",
"Nilanjan",
""
]
] |
new_dataset
| 0.96566 |
2207.02506
|
Evangelos Bitsikas
|
Evangelos Bitsikas and Christina P\"opper
|
You have been warned: Abusing 5G's Warning and Emergency Systems
| null | null |
10.1145/3564625.3568000
| null |
cs.CR cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Public Warning System (PWS) is an essential part of cellular networks and
a country's civil protection. Warnings can notify users of hazardous events
(e.g., floods, earthquakes) and crucial national matters that require immediate
attention. PWS attacks disseminating fake warnings or concealing precarious
events can have a serious impact, causing fraud, panic, physical harm, or
unrest to users within an affected area. In this work, we conduct the first
comprehensive investigation of PWS security in 5G networks. We demonstrate five
practical attacks that may impact the security of 5G-based Commercial Mobile
Alert System (CMAS) as well as Earthquake and Tsunami Warning System (ETWS)
alerts. Additional to identifying the vulnerabilities, we investigate two PWS
spoofing and three PWS suppression attacks, with or without a man-in-the-middle
(MitM) attacker. We discover that MitM-based attacks have more severe impact
than their non-MitM counterparts. Our PWS barring attack is an effective
technique to eliminate legitimate warning messages. We perform a rigorous
analysis of the roaming aspect of the PWS, incl. its potentially secure
version, and report the implications of our attacks on other emergency features
(e.g., 911 SIP calls). We discuss possible countermeasures and note that
eradicating the attacks necessitates a scrupulous reevaluation of the PWS
design and a secure implementation.
|
[
{
"version": "v1",
"created": "Wed, 6 Jul 2022 08:15:12 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 14:29:19 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Bitsikas",
"Evangelos",
""
],
[
"Pöpper",
"Christina",
""
]
] |
new_dataset
| 0.997224 |
2210.02318
|
C\'edric Picron
|
C\'edric Picron, Punarjay Chakravarty, Tinne Tuytelaars
|
FQDet: Fast-converging Query-based Detector
|
Accepted at NeurIPS VTTA workshop 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, two-stage Deformable DETR introduced the query-based two-stage
head, a new type of two-stage head different from the region-based two-stage
heads of classical detectors as Faster R-CNN. In query-based two-stage heads,
the second stage selects one feature per detection processed by a transformer,
called the query, as opposed to pooling a rectangular grid of features
processed by CNNs as in region-based detectors. In this work, we improve the
query-based head by improving the prior of the cross-attention operation with
anchors, significantly speeding up the convergence while increasing its
performance. Additionally, we empirically show that by improving the
cross-attention prior, auxiliary losses and iterative bounding box mechanisms
typically used by DETR-based detectors are no longer needed. By combining the
best of both the classical and the DETR-based detectors, our FQDet head peaks
at 45.4 AP on the 2017 COCO validation set when using a ResNet-50+TPN backbone,
only after training for 12 epochs using the 1x schedule. We outperform other
high-performing two-stage heads such as e.g. Cascade R-CNN, while using the
same backbone and while being computationally cheaper. Additionally, when using
the large ResNeXt-101-DCN+TPN backbone and multi-scale testing, our FQDet head
achieves 52.9 AP on the 2017 COCO test-dev set after only 12 epochs of
training. Code is released at https://github.com/CedricPicron/FQDet .
|
[
{
"version": "v1",
"created": "Wed, 5 Oct 2022 15:19:34 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 08:05:18 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Picron",
"Cédric",
""
],
[
"Chakravarty",
"Punarjay",
""
],
[
"Tuytelaars",
"Tinne",
""
]
] |
new_dataset
| 0.974571 |
2210.09482
|
Sri Hrushikesh Varma Bhupathiraju
|
Yulong Cao, S. Hrushikesh Bhupathiraju, Pirouz Naghavi, Takeshi
Sugawara, Z. Morley Mao, Sara Rampazzi
|
You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous
Vehicles Driving Frameworks
|
Accepted to the 32nd USENIX Security Symposium (2023)
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Autonomous Vehicles (AVs) increasingly use LiDAR-based object detection
systems to perceive other vehicles and pedestrians on the road. While existing
attacks on LiDAR-based autonomous driving architectures focus on lowering the
confidence score of AV object detection models to induce obstacle misdetection,
our research discovers how to leverage laser-based spoofing techniques to
selectively remove the LiDAR point cloud data of genuine obstacles at the
sensor level before being used as input to the AV perception. The ablation of
this critical LiDAR information causes autonomous driving obstacle detectors to
fail to identify and locate obstacles and, consequently, induces AVs to make
dangerous automatic driving decisions. In this paper, we present a method
invisible to the human eye that hides objects and deceives autonomous vehicles'
obstacle detectors by exploiting inherent automatic transformation and
filtering processes of LiDAR sensor data integrated with autonomous driving
frameworks. We call such attacks Physical Removal Attacks (PRA), and we
demonstrate their effectiveness against three popular AV obstacle detectors
(Apollo, Autoware, PointPillars), and we achieve 45{\deg} attack capability. We
evaluate the attack impact on three fusion models (Frustum-ConvNet, AVOD, and
Integrated-Semantic Level Fusion) and the consequences on the driving decision
using LGSVL, an industry-grade simulator. In our moving vehicle scenarios, we
achieve a 92.7% success rate removing 90\% of a target obstacle's cloud points.
Finally, we demonstrate the attack's success against two popular defenses
against spoofing and object hiding attacks and discuss two enhanced defense
strategies to mitigate our attack.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 00:02:00 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 18:43:03 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Cao",
"Yulong",
""
],
[
"Bhupathiraju",
"S. Hrushikesh",
""
],
[
"Naghavi",
"Pirouz",
""
],
[
"Sugawara",
"Takeshi",
""
],
[
"Mao",
"Z. Morley",
""
],
[
"Rampazzi",
"Sara",
""
]
] |
new_dataset
| 0.982191 |
2210.12756
|
Andreas Georgis
|
Andreas Georgis, Panagiotis Mermigkas, Petros Maragos
|
VP-SLAM: A Monocular Real-time Visual SLAM with Points, Lines and
Vanishing Points
| null | null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Traditional monocular Visual Simultaneous Localization and Mapping (vSLAM)
systems can be divided into three categories: those that use features, those
that rely on the image itself, and hybrid models. In the case of feature-based
methods, new research has evolved to incorporate more information from their
environment using geometric primitives beyond points, such as lines and planes.
This is because in many environments, which are man-made environments,
characterized as Manhattan world, geometric primitives such as lines and planes
occupy most of the space in the environment. The exploitation of these schemes
can lead to the introduction of algorithms capable of optimizing the trajectory
of a Visual SLAM system and also helping to construct an exuberant map. Thus,
we present a real-time monocular Visual SLAM system that incorporates real-time
methods for line and VP extraction, as well as two strategies that exploit
vanishing points to estimate the robot's translation and improve its
rotation.Particularly, we build on ORB-SLAM2, which is considered the current
state-of-the-art solution in terms of both accuracy and efficiency, and extend
its formulation to handle lines and VPs to create two strategies the first
optimize the rotation and the second refine the translation part from the known
rotation. First, we extract VPs using a real-time method and use them for a
global rotation optimization strategy. Second, we present a translation
estimation method that takes advantage of last-stage rotation optimization to
model a linear system. Finally, we evaluate our system on the TUM RGB-D
benchmark and demonstrate that the proposed system achieves state-of-the-art
results and runs in real time, and its performance remains close to the
original ORB-SLAM2 system
|
[
{
"version": "v1",
"created": "Sun, 23 Oct 2022 15:54:26 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 10:29:20 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Georgis",
"Andreas",
""
],
[
"Mermigkas",
"Panagiotis",
""
],
[
"Maragos",
"Petros",
""
]
] |
new_dataset
| 0.993192 |
2210.15306
|
Rodrigo Diaz
|
Rodrigo Diaz, Ben Hayes, Charalampos Saitis, Gy\"orgy Fazekas, Mark
Sandler
|
Rigid-Body Sound Synthesis with Differentiable Modal Resonators
|
5 pages
| null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Physical models of rigid bodies are used for sound synthesis in applications
from virtual environments to music production. Traditional methods such as
modal synthesis often rely on computationally expensive numerical solvers,
while recent deep learning approaches are limited by post-processing of their
results. In this work we present a novel end-to-end framework for training a
deep neural network to generate modal resonators for a given 2D shape and
material, using a bank of differentiable IIR filters. We demonstrate our method
on a dataset of synthetic objects, but train our model using an audio-domain
objective, paving the way for physically-informed synthesisers to be learned
directly from recordings of real-world objects.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 10:34:38 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 11:47:41 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Diaz",
"Rodrigo",
""
],
[
"Hayes",
"Ben",
""
],
[
"Saitis",
"Charalampos",
""
],
[
"Fazekas",
"György",
""
],
[
"Sandler",
"Mark",
""
]
] |
new_dataset
| 0.999783 |
2210.15436
|
Giulia Cavicchioni
|
Giulia Cavicchioni, Alessio Meneghetti
|
The weight distribution of codes over finite chain rings
| null | null | null | null |
cs.IT math.AC math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this work, we determine new linear equations for the weight distribution
of linear codes over finite chain rings. The identities are determined by
counting the number of some special submatrices of the parity-check matrix of
the code. Thanks to these relations we are able to compute the full weight
distribution of codes with small Singleton defects, such as MDS, MDR and AMDR
codes.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 13:58:13 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Oct 2022 07:21:52 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Cavicchioni",
"Giulia",
""
],
[
"Meneghetti",
"Alessio",
""
]
] |
new_dataset
| 0.999205 |
2210.15696
|
Everlyn Chimoto
|
Everlyn Asiko Chimoto and Bruce A. Bassett
|
COMET-QE and Active Learning for Low-Resource Machine Translation
|
Accepted to Findings of EMNLP 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Active learning aims to deliver maximum benefit when resources are scarce. We
use COMET-QE, a reference-free evaluation metric, to select sentences for
low-resource neural machine translation. Using Swahili, Kinyarwanda and Spanish
for our experiments, we show that COMET-QE significantly outperforms two
variants of Round Trip Translation Likelihood (RTTL) and random sentence
selection by up to 5 BLEU points for 20k sentences selected by Active Learning
on a 30k baseline. This suggests that COMET-QE is a powerful tool for sentence
selection in the very low-resource limit.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 18:00:41 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Chimoto",
"Everlyn Asiko",
""
],
[
"Bassett",
"Bruce A.",
""
]
] |
new_dataset
| 0.989994 |
2210.15722
|
Sachin Chhabra
|
Sachin Chhabra, Prabal Bijoy Dutta, Hemanth Venkateswara and Baoxin Li
|
PatchRot: A Self-Supervised Technique for Training Vision Transformers
|
NeurIPS Workshop on Vision Transformers: Theory and Applications
(VTTA)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision transformers require a huge amount of labeled data to outperform
convolutional neural networks. However, labeling a huge dataset is a very
expensive process. Self-supervised learning techniques alleviate this problem
by learning features similar to supervised learning in an unsupervised way. In
this paper, we propose a self-supervised technique PatchRot that is crafted for
vision transformers. PatchRot rotates images and image patches and trains the
network to predict the rotation angles. The network learns to extract both
global and local features from an image. Our extensive experiments on different
datasets showcase PatchRot training learns rich features which outperform
supervised learning and compared baseline.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 18:55:12 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Chhabra",
"Sachin",
""
],
[
"Dutta",
"Prabal Bijoy",
""
],
[
"Venkateswara",
"Hemanth",
""
],
[
"Li",
"Baoxin",
""
]
] |
new_dataset
| 0.999661 |
2210.15769
|
Giacomo Fiorentini
|
Giacomo Fiorentini, Itir Onal Ertugrul, Albert Ali Salah
|
Fully-attentive and interpretable: vision and video vision transformers
for pain detection
|
9 pages (12 with references), 10 figures, VTTA2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Pain is a serious and costly issue globally, but to be treated, it must first
be detected. Vision transformers are a top-performing architecture in computer
vision, with little research on their use for pain detection. In this paper, we
propose the first fully-attentive automated pain detection pipeline that
achieves state-of-the-art performance on binary pain detection from facial
expressions. The model is trained on the UNBC-McMaster dataset, after faces are
3D-registered and rotated to the canonical frontal view. In our experiments we
identify important areas of the hyperparameter space and their interaction with
vision and video vision transformers, obtaining 3 noteworthy models. We analyse
the attention maps of one of our models, finding reasonable interpretations for
its predictions. We also evaluate Mixup, an augmentation technique, and
Sharpness-Aware Minimization, an optimizer, with no success. Our presented
models, ViT-1 (F1 score 0.55 +- 0.15), ViViT-1 (F1 score 0.55 +- 0.13), and
ViViT-2 (F1 score 0.49 +- 0.04), all outperform earlier works, showing the
potential of vision transformers for pain detection. Code is available at
https://github.com/IPDTFE/ViT-McMaster
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 21:01:40 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Fiorentini",
"Giacomo",
""
],
[
"Ertugrul",
"Itir Onal",
""
],
[
"Salah",
"Albert Ali",
""
]
] |
new_dataset
| 0.99907 |
2210.15790
|
Lin Zhao
|
Heng Huang, Lin Zhao, Xintao Hu, Haixing Dai, Lu Zhang, Dajiang Zhu,
Tianming Liu
|
BI AVAN: Brain inspired Adversarial Visual Attention Network
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Visual attention is a fundamental mechanism in the human brain, and it
inspires the design of attention mechanisms in deep neural networks. However,
most of the visual attention studies adopted eye-tracking data rather than the
direct measurement of brain activity to characterize human visual attention. In
addition, the adversarial relationship between the attention-related objects
and attention-neglected background in the human visual system was not fully
exploited. To bridge these gaps, we propose a novel brain-inspired adversarial
visual attention network (BI-AVAN) to characterize human visual attention
directly from functional brain activity. Our BI-AVAN model imitates the biased
competition process between attention-related/neglected objects to identify and
locate the visual objects in a movie frame the human brain focuses on in an
unsupervised manner. We use independent eye-tracking data as ground truth for
validation and experimental results show that our model achieves robust and
promising results when inferring meaningful human visual attention and mapping
the relationship between brain activities and visual stimuli. Our BI-AVAN model
contributes to the emerging field of leveraging the brain's functional
architecture to inspire and guide the model design in artificial intelligence
(AI), e.g., deep neural networks.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 22:20:36 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Huang",
"Heng",
""
],
[
"Zhao",
"Lin",
""
],
[
"Hu",
"Xintao",
""
],
[
"Dai",
"Haixing",
""
],
[
"Zhang",
"Lu",
""
],
[
"Zhu",
"Dajiang",
""
],
[
"Liu",
"Tianming",
""
]
] |
new_dataset
| 0.980615 |
2210.15791
|
Shaunak Mehta
|
Shaunak A. Mehta, Yeunhee Kim, Joshua Hoegerman, Michael D. Bartlett
and Dylan P. Losey
|
RISO: Combining Rigid Grippers with Soft Switchable Adhesives
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robot arms that assist humans should be able to pick up, move, and release
everyday objects. Today's assistive robot arms use rigid grippers to pinch
items between fingers; while these rigid grippers are well suited for large and
heavy objects, they often struggle to grasp small, numerous, or delicate items
(such as foods). Soft grippers cover the opposite end of the spectrum; these
grippers use adhesives or change shape to wrap around small and irregular
items, but cannot exert the large forces needed to manipulate heavy objects. In
this paper we introduce RIgid-SOft (RISO) grippers that combine switchable soft
adhesives with standard rigid mechanisms to enable a diverse range of robotic
grasping. We develop RISO grippers by leveraging a novel class of soft
materials that change adhesion force in real-time through pneumatically
controlled shape and rigidity tuning. By mounting these soft adhesives on the
bottom of rigid fingers, we create a gripper that can interact with objects
using either purely rigid grasps (pinching the object) or purely soft grasps
(adhering to the object). This increased capability requires additional
decision making, and we therefore formulate a shared control approach that
partially automates the motion of the robot arm. In practice, this controller
aligns the RISO gripper while inferring which object the human wants to grasp
and how the human wants to grasp that item. Our user study demonstrates that
RISO grippers can pick up, move, and release household items from existing
datasets, and that the system performs grasps more successfully and efficiently
when sharing control between the human and robot. See videos here:
https://youtu.be/5uLUkBYcnwg
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 22:26:15 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Mehta",
"Shaunak A.",
""
],
[
"Kim",
"Yeunhee",
""
],
[
"Hoegerman",
"Joshua",
""
],
[
"Bartlett",
"Michael D.",
""
],
[
"Losey",
"Dylan P.",
""
]
] |
new_dataset
| 0.994652 |
2210.15852
|
Todd Murphey
|
Joel Meyer, Allison Pinosky, Thomas Trzpit, Ed Colgate, Todd D.
Murphey
|
A Game Benchmark for Real-Time Human-Swarm Control
|
8 pages, IEEE Conference on Automation Science and Engineering
(CASE), 2022
| null | null | null |
cs.RO cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a game benchmark for testing human-swarm control algorithms and
interfaces in a real-time, high-cadence scenario. Our benchmark consists of a
swarm vs. swarm game in a virtual ROS environment in which the goal of the game
is to capture all agents from the opposing swarm; the game's high-cadence is a
result of the capture rules, which cause agent team sizes to fluctuate rapidly.
These rules require players to consider both the number of agents currently at
their disposal and the behavior of their opponent's swarm when they plan
actions. We demonstrate our game benchmark with a default human-swarm control
system that enables a player to interact with their swarm through a high-level
touchscreen interface. The touchscreen interface transforms player gestures
into swarm control commands via a low-level decentralized ergodic control
framework. We compare our default human-swarm control system to a
flocking-based control system, and discuss traits that are crucial for swarm
control algorithms and interfaces operating in real-time, high-cadence
scenarios like our game benchmark. Our game benchmark code is available on
Github; more information can be found at
https://sites.google.com/view/swarm-game-benchmark.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 02:47:14 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Meyer",
"Joel",
""
],
[
"Pinosky",
"Allison",
""
],
[
"Trzpit",
"Thomas",
""
],
[
"Colgate",
"Ed",
""
],
[
"Murphey",
"Todd D.",
""
]
] |
new_dataset
| 0.999786 |
2210.15907
|
Devansh Jalota
|
Devansh Jalota and Jessica Lazarus and Alexandre Bayen and Marco
Pavone
|
Credit-Based Congestion Pricing: Equilibrium Properties and Optimal
Scheme Design
| null | null | null | null |
cs.GT cs.MA math.OC
|
http://creativecommons.org/licenses/by/4.0/
|
Credit-based congestion pricing (CBCP) has emerged as a mechanism to
alleviate the social inequity concerns of road congestion pricing - a promising
strategy for traffic congestion mitigation - by providing low-income users with
travel credits to offset some of their toll payments. While CBCP offers immense
potential for addressing inequity issues that hamper the practical viability of
congestion pricing, the deployment of CBCP in practice is nascent, and the
potential efficacy and optimal design of CBCP schemes have yet to be
formalized. In this work, we study the design of CBCP schemes to achieve
particular societal objectives and investigate their influence on traffic
patterns when routing heterogeneous users with different values of time (VoTs)
in a multi-lane highway with an express lane. We introduce a new non-atomic
congestion game model of a mixed-economy, wherein eligible users receive travel
credits while the remaining ineligible users pay out-of-pocket to use the
express lane. In this setting, we investigate the effect of CBCP schemes on
traffic patterns by characterizing the properties (i.e., existence, comparative
statics) of the corresponding Nash equilibria and, in the setting when eligible
users have time-invariant VoTs, develop a convex program to compute these
equilibria. We further present a bi-level optimization framework to design
optimal CBCP schemes to achieve a central planner's societal objectives.
Finally, we conduct numerical experiments based on a case study of the San
Mateo 101 Express Lanes Project, one of the first North American CBCP pilots.
Our results demonstrate the potential of CBCP to enable low-income travelers to
avail of the travel time savings provided by congestion pricing on express
lanes while having comparatively low impacts on the travel costs of other road
users.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 05:29:10 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Jalota",
"Devansh",
""
],
[
"Lazarus",
"Jessica",
""
],
[
"Bayen",
"Alexandre",
""
],
[
"Pavone",
"Marco",
""
]
] |
new_dataset
| 0.998347 |
2210.15913
|
Zhaowei Chen
|
Zhaowei Chen, Peng Li, Zeyong Wei, Honghua Chen, Haoran Xie, Mingqiang
Wei, Fu Lee Wang
|
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud
Denoising
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We propose GeoGCN, a novel geometric dual-domain graph convolution network
for point cloud denoising (PCD). Beyond the traditional wisdom of PCD, to fully
exploit the geometric information of point clouds, we define two kinds of
surface normals, one is called Real Normal (RN), and the other is Virtual
Normal (VN). RN preserves the local details of noisy point clouds while VN
avoids the global shape shrinkage during denoising. GeoGCN is a new PCD
paradigm that, 1) first regresses point positions by spatialbased GCN with the
help of VNs, 2) then estimates initial RNs by performing Principal Component
Analysis on the regressed points, and 3) finally regresses fine RNs by
normalbased GCN. Unlike existing PCD methods, GeoGCN not only exploits two
kinds of geometry expertise (i.e., RN and VN) but also benefits from training
data. Experiments validate that GeoGCN outperforms SOTAs in terms of both
noise-robustness and local-and-global feature preservation.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 05:48:57 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Chen",
"Zhaowei",
""
],
[
"Li",
"Peng",
""
],
[
"Wei",
"Zeyong",
""
],
[
"Chen",
"Honghua",
""
],
[
"Xie",
"Haoran",
""
],
[
"Wei",
"Mingqiang",
""
],
[
"Wang",
"Fu Lee",
""
]
] |
new_dataset
| 0.97894 |
2210.15933
|
Baian Chen
|
Baian Chen, Lipeng Gu, Xin Zhuang, Yiyang Shen, Weiming Wang,
Mingqiang Wei
|
PSFormer: Point Transformer for 3D Salient Object Detection
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We propose PSFormer, an effective point transformer model for 3D salient
object detection. PSFormer is an encoder-decoder network that takes full
advantage of transformers to model the contextual information in both
multi-scale point- and scene-wise manners. In the encoder, we develop a Point
Context Transformer (PCT) module to capture region contextual features at the
point level; PCT contains two different transformers to excavate the
relationship among points. In the decoder, we develop a Scene Context
Transformer (SCT) module to learn context representations at the scene level;
SCT contains both Upsampling-and-Transformer blocks and Multi-context
Aggregation units to integrate the global semantic and multi-level features
from the encoder into the global scene context. Experiments show clear
improvements of PSFormer over its competitors and validate that PSFormer is
more robust to challenging cases such as small objects, multiple objects, and
objects with complex structures.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 06:34:28 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Chen",
"Baian",
""
],
[
"Gu",
"Lipeng",
""
],
[
"Zhuang",
"Xin",
""
],
[
"Shen",
"Yiyang",
""
],
[
"Wang",
"Weiming",
""
],
[
"Wei",
"Mingqiang",
""
]
] |
new_dataset
| 0.997081 |
2210.15937
|
Atsushi Ando
|
Atsushi Ando, Ryo Masumura, Akihiko Takashima, Satoshi Suzuki, Naoki
Makishima, Keita Suzuki, Takafumi Moriya, Takanori Ashihara, Hiroshi Sato
|
On the Use of Modality-Specific Large-Scale Pre-Trained Encoders for
Multimodal Sentiment Analysis
|
Accepted to SLT 2022
| null | null | null |
cs.CL cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper investigates the effectiveness and implementation of
modality-specific large-scale pre-trained encoders for multimodal sentiment
analysis~(MSA). Although the effectiveness of pre-trained encoders in various
fields has been reported, conventional MSA methods employ them for only
linguistic modality, and their application has not been investigated. This
paper compares the features yielded by large-scale pre-trained encoders with
conventional heuristic features. One each of the largest pre-trained encoders
publicly available for each modality are used; CLIP-ViT, WavLM, and BERT for
visual, acoustic, and linguistic modalities, respectively. Experiments on two
datasets reveal that methods with domain-specific pre-trained encoders attain
better performance than those with conventional features in both unimodal and
multimodal scenarios. We also find it better to use the outputs of the
intermediate layers of the encoders than those of the output layer. The codes
are available at https://github.com/ando-hub/MSA_Pretrain.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 06:48:35 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Ando",
"Atsushi",
""
],
[
"Masumura",
"Ryo",
""
],
[
"Takashima",
"Akihiko",
""
],
[
"Suzuki",
"Satoshi",
""
],
[
"Makishima",
"Naoki",
""
],
[
"Suzuki",
"Keita",
""
],
[
"Moriya",
"Takafumi",
""
],
[
"Ashihara",
"Takanori",
""
],
[
"Sato",
"Hiroshi",
""
]
] |
new_dataset
| 0.969997 |
2210.15939
|
Shaoshan Liu
|
Tianze Wu, Shaoshan Liu, Bo Yu, Sa Wang, Yungang Bao, Weisong Shi
|
INTERNEURON: A Middleware with Multi-Network Communication Reliability
for Infrastructure Vehicle Cooperative Autonomous Driving
| null | null | null | null |
cs.RO cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Infrastructure-Vehicle Cooperative Autonomous Driving (IVCAD) is a new
paradigm of autonomous driving, which relies on the cooperation between
intelligent roads and autonomous vehicles. This paradigm has been shown to be
safer and more efficient compared to the on-vehicle-only autonomous driving
paradigm. Our real-world deployment data indicates that the effectiveness of
IVCAD is constrained by reliability and performance of commercial communication
networks. This paper targets this exact problem, and proposes INTERNEURON, a
middleware to achieve high communication reliability between intelligent roads
and autonomous vehicles, in the context of IVCAD. Specifically, INTERNEURON
dynamically matches IVCAD applications and the underlying communication
technologies based on varying communication performance and quality needs.
Evaluation results confirm that INTERNEURON reduces deadline violations by more
than 95\%, significantly improving the reliability of IVCAD systems.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 06:56:36 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Wu",
"Tianze",
""
],
[
"Liu",
"Shaoshan",
""
],
[
"Yu",
"Bo",
""
],
[
"Wang",
"Sa",
""
],
[
"Bao",
"Yungang",
""
],
[
"Shi",
"Weisong",
""
]
] |
new_dataset
| 0.999599 |
2210.15954
|
Jonathan Zheng
|
Jonathan Zheng, Ashutosh Baheti, Tarek Naous, Wei Xu, and Alan Ritter
|
Stanceosaurus: Classifying Stance Towards Multilingual Misinformation
|
Accepted to EMNLP 2022 main conference
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi,
and Arabic annotated with stance towards 251 misinformation claims. As far as
we are aware, it is the largest corpus annotated with stance towards
misinformation claims. The claims in Stanceosaurus originate from 15
fact-checking sources that cover diverse geographical regions and cultures.
Unlike existing stance datasets, we introduce a more fine-grained 5-class
labeling strategy with additional subcategories to distinguish implicit stance.
Pre-trained transformer-based stance classifiers that are fine-tuned on our
corpus show good generalization on unseen claims and regional claims from
countries outside the training data. Cross-lingual experiments demonstrate
Stanceosaurus' capability of training multi-lingual models, achieving 53.1 F1
on Hindi and 50.4 F1 on Arabic without any target-language fine-tuning.
Finally, we show how a domain adaptation method can be used to improve
performance on Stanceosaurus using additional RumourEval-2019 data. We make
Stanceosaurus publicly available to the research community and hope it will
encourage further work on misinformation identification across languages and
cultures.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 07:18:32 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Zheng",
"Jonathan",
""
],
[
"Baheti",
"Ashutosh",
""
],
[
"Naous",
"Tarek",
""
],
[
"Xu",
"Wei",
""
],
[
"Ritter",
"Alan",
""
]
] |
new_dataset
| 0.994884 |
2210.15972
|
Yan Zhang
|
Yan Zhang, Xiyuan Gao, Qingyan Duan, Jiaxu Leng, Xiao Pu, Xinbo Gao
|
Contextual Learning in Fourier Complex Field for VHR Remote Sensing
Images
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Very high-resolution (VHR) remote sensing (RS) image classification is the
fundamental task for RS image analysis and understanding. Recently,
transformer-based models demonstrated outstanding potential for learning
high-order contextual relationships from natural images with general resolution
(224x224 pixels) and achieved remarkable results on general image
classification tasks. However, the complexity of the naive transformer grows
quadratically with the increase in image size, which prevents transformer-based
models from VHR RS image (500x500 pixels) classification and other
computationally expensive downstream tasks. To this end, we propose to
decompose the expensive self-attention (SA) into real and imaginary parts via
discrete Fourier transform (DFT) and therefore propose an efficient complex
self-attention (CSA) mechanism. Benefiting from the conjugated symmetric
property of DFT, CSA is capable to model the high-order contextual information
with less than half computations of naive SA. To overcome the gradient
explosion in Fourier complex field, we replace the Softmax function with the
carefully designed Logmax function to normalize the attention map of CSA and
stabilize the gradient propagation. By stacking various layers of CSA blocks,
we propose the Fourier Complex Transformer (FCT) model to learn global
contextual information from VHR aerial images following the hierarchical
manners. Universal experiments conducted on commonly used RS classification
data sets demonstrate the effectiveness and efficiency of FCT, especially on
very high-resolution RS images.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 08:13:33 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Zhang",
"Yan",
""
],
[
"Gao",
"Xiyuan",
""
],
[
"Duan",
"Qingyan",
""
],
[
"Leng",
"Jiaxu",
""
],
[
"Pu",
"Xiao",
""
],
[
"Gao",
"Xinbo",
""
]
] |
new_dataset
| 0.970514 |
2210.15988
|
Leyi Zhao Ennard
|
Leyi Zhao, Yi Li
|
Spectrograms Are Sequences of Patches
| null | null | null | null |
cs.SD cs.AI cs.MM eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Self-supervised pre-training models have been used successfully in several
machine learning domains. However, only a tiny amount of work is related to
music. In our work, we treat a spectrogram of music as a series of patches and
design a self-supervised model that captures the features of these sequential
patches: Patchifier, which makes good use of self-supervised learning methods
from both NLP and CV domains. We do not use labeled data for the pre-training
process, only a subset of the MTAT dataset containing 16k music clips. After
pre-training, we apply the model to several downstream tasks. Our model
achieves a considerably acceptable result compared to other audio
representation models. Meanwhile, our work demonstrates that it makes sense to
consider audio as a series of patch segments.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 08:39:36 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Zhao",
"Leyi",
""
],
[
"Li",
"Yi",
""
]
] |
new_dataset
| 0.96104 |
2210.15999
|
Ayman Beghdadi
|
Ayman Beghdadi, Malik Mallem, Lotfi Beji
|
Benchmarking performance of object detection under image distortions in
an uncontrolled environment
| null | null | null | null |
cs.CV cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
The robustness of object detection algorithms plays a prominent role in
real-world applications, especially in uncontrolled environments due to
distortions during image acquisition. It has been proven that the performance
of object detection methods suffers from in-capture distortions. In this study,
we present a performance evaluation framework for the state-of-the-art object
detection methods using a dedicated dataset containing images with various
distortions at different levels of severity. Furthermore, we propose an
original strategy of image distortion generation applied to the MS-COCO dataset
that combines some local and global distortions to reach much better
performances. We have shown that training using the proposed dataset improves
the robustness of object detection by 31.5\%. Finally, we provide a custom
dataset including natural images distorted from MS-COCO to perform a more
reliable evaluation of the robustness against common distortions. The database
and the generation source codes of the different distortions are made publicly
available
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 09:06:52 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Beghdadi",
"Ayman",
""
],
[
"Mallem",
"Malik",
""
],
[
"Beji",
"Lotfi",
""
]
] |
new_dataset
| 0.99229 |
2210.16018
|
Ekaterina Trofimova
|
Anastasia Drozdova, Polina Guseva, Ekaterina Trofimova, Anna
Scherbakova, Andrey Ustyuzhanin
|
Code4ML: a Large-scale Dataset of annotated Machine Learning Code
|
Under review
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Program code as a data source is gaining popularity in the data science
community. Possible applications for models trained on such assets range from
classification for data dimensionality reduction to automatic code generation.
However, without annotation number of methods that could be applied is somewhat
limited. To address the lack of annotated datasets, we present the Code4ML
corpus. It contains code snippets, task summaries, competitions and dataset
descriptions publicly available from Kaggle - the leading platform for hosting
data science competitions. The corpus consists of ~2.5 million snippets of ML
code collected from ~100 thousand Jupyter notebooks. A representative fraction
of the snippets is annotated by human assessors through a user-friendly
interface specially designed for that purpose. Code4ML dataset can potentially
help address a number of software engineering or data science challenges
through a data-driven approach. For example, it can be helpful for semantic
code classification, code auto-completion, and code generation for an ML task
specified in natural language.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 09:44:19 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Drozdova",
"Anastasia",
""
],
[
"Guseva",
"Polina",
""
],
[
"Trofimova",
"Ekaterina",
""
],
[
"Scherbakova",
"Anna",
""
],
[
"Ustyuzhanin",
"Andrey",
""
]
] |
new_dataset
| 0.999811 |
2210.16029
|
Shaoguang Mao
|
Zhiyi Wang, Shaoguang Mao, Wenshan Wu, Yan Xia
|
Assessing Phrase Break of ESL speech with Pre-trained Language Models
|
Under Review, ICASSP 2023
| null | null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This work introduces an approach to assessing phrase break in ESL learners'
speech with pre-trained language models (PLMs). Different with traditional
methods, this proposal converts speech to token sequences, and then leverages
the power of PLMs. There are two sub-tasks: overall assessment of phrase break
for a speech clip; fine-grained assessment of every possible phrase break
position. Speech input is first force-aligned with texts, then pre-processed to
a token sequence, including words and associated phrase break information. The
token sequence is then fed into the pre-training and fine-tuning pipeline. In
pre-training, a replaced break token detection module is trained with token
data where each token has a certain percentage chance to be randomly replaced.
In fine-tuning, overall and fine-grained scoring are optimized with text
classification and sequence labeling pipeline, respectively. With the
introduction of PLMs, the dependence on labeled training data has been greatly
reduced, and performance has improved.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 10:06:06 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Wang",
"Zhiyi",
""
],
[
"Mao",
"Shaoguang",
""
],
[
"Wu",
"Wenshan",
""
],
[
"Xia",
"Yan",
""
]
] |
new_dataset
| 0.960751 |
2210.16063
|
Roee Mordechai Francos
|
Roee M. Francos and Alfred M. Bruckstein
|
Defense Against Smart Invaders with Swarms of Sweeping Agents
|
18 pages, 21 figures
| null | null | null |
cs.MA
|
http://creativecommons.org/licenses/by/4.0/
|
The goal of this research is to devise guaranteed defense policies that allow
to protect a given region from the entrance of smart mobile invaders by
detecting them using a team of defending agents equipped with identical line
sensors. By designing cooperative defense strategies that ensure all invaders
are detected, conditions on the defenders' speed are derived. Successful
accomplishment of the defense task implies invaders with a known limit on their
speed cannot slip past the defenders and enter the guarded region undetected.
The desired outcome of the defense protocols is to defend the area and
additionally to expand it as much as possible. Expansion becomes possible if
the defenders' speed exceeds a critical speed that is necessary to only defend
the initial region. We present results on the total search time, critical
speeds and maximal expansion possible for two types of novel pincer-movement
defense processes, circular and spiral, for any even number of defenders. The
proposed spiral process allows to detect invaders at nearly the lowest
theoretically optimal speed, and if this speed is exceeded, it also allows to
expand the protected region almost to the maximal area.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 11:18:08 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Francos",
"Roee M.",
""
],
[
"Bruckstein",
"Alfred M.",
""
]
] |
new_dataset
| 0.988101 |
2210.16083
|
JunKyu Lee
|
JunKyu Lee, Blesson Varghese, Hans Vandierendonck
|
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
|
Accepted at the IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV) 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper analyzes the effects of dynamically varying video contents and
detection latency on the real-time detection accuracy of a detector and
proposes a new run-time accuracy variation model, ROMA, based on the findings
from the analysis. ROMA is designed to select an optimal detector out of a set
of detectors in real time without label information to maximize real-time
object detection accuracy. ROMA utilizing four YOLOv4 detectors on an NVIDIA
Jetson Nano shows real-time accuracy improvements by 4 to 37% for a scenario of
dynamically varying video contents and detection latency consisting of MOT17Det
and MOT20Det datasets, compared to individual YOLOv4 detectors and two
state-of-the-art runtime techniques.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 12:06:29 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Lee",
"JunKyu",
""
],
[
"Varghese",
"Blesson",
""
],
[
"Vandierendonck",
"Hans",
""
]
] |
new_dataset
| 0.951412 |
2210.16204
|
Nicola Marinello
|
Nicola Marinello (1), Marc Proesmans (1 and 3), Luc Van Gool (1 and 2
and 3) ((1) KU Leuven/ESAT-PSI, (2) ETH Zurich/CVL, (3) TRACE vzw)
|
TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM
|
Accepted to CVPR 2022 Workshop on Autonomous Driving
|
Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR) Workshops June 2022 4500-4510
|
10.1109/CVPRW56347.2022.00496
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
3D object tracking is a critical task in autonomous driving systems. It plays
an essential role for the system's awareness about the surrounding environment.
At the same time there is an increasing interest in algorithms for autonomous
cars that solely rely on inexpensive sensors, such as cameras. In this paper we
investigate the use of triplet embeddings in combination with motion
representations for 3D object tracking. We start from an off-the-shelf 3D
object detector, and apply a tracking mechanism where objects are matched by an
affinity score computed on local object feature embeddings and motion
descriptors. The feature embeddings are trained to include information about
the visual appearance and monocular 3D object characteristics, while motion
descriptors provide a strong representation of object trajectories. We will
show that our approach effectively re-identifies objects, and also behaves
reliably and accurately in case of occlusions, missed detections and can detect
re-appearance across different field of views. Experimental evaluation shows
that our approach outperforms state-of-the-art on nuScenes by a large margin.
We also obtain competitive results on KITTI.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 15:23:50 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Marinello",
"Nicola",
"",
"KU Leuven/ESAT-PSI"
],
[
"Proesmans",
"Marc",
"",
"1 and 3"
],
[
"Van Gool",
"Luc",
"",
"1 and 2\n and 3"
]
] |
new_dataset
| 0.998687 |
2210.16231
|
Sergey Novoselov
|
Sergey Novoselov, Vladimir Volokhov, Galina Lavrentyeva
|
Universal speaker recognition encoders for different speech segments
duration
|
Submitted to ICASSP'23
| null | null | null |
cs.SD cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Creating universal speaker encoders which are robust for different acoustic
and speech duration conditions is a big challenge today. According to our
observations systems trained on short speech segments are optimal for short
phrase speaker verification and systems trained on long segments are superior
for long segments verification. A system trained simultaneously on pooled short
and long speech segments does not give optimal verification results and usually
degrades both for short and long segments. This paper addresses the problem of
creating universal speaker encoders for different speech segments duration. We
describe our simple recipe for training universal speaker encoder for any type
of selected neural network architecture. According to our evaluation results of
wav2vec-TDNN based systems obtained for NIST SRE and VoxCeleb1 benchmarks the
proposed universal encoder provides speaker verification improvements in case
of different enrollment and test speech segment duration. The key feature of
the proposed encoder is that it has the same inference time as the selected
neural network architecture.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 16:06:00 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Novoselov",
"Sergey",
""
],
[
"Volokhov",
"Vladimir",
""
],
[
"Lavrentyeva",
"Galina",
""
]
] |
new_dataset
| 0.994583 |
2210.16253
|
Mattia Pugliatti
|
Mattia Pugliatti and Francesco Topputo
|
DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and
Blender setup
|
16 pages, 19 figures, summary paper of a dataset
| null | null | null |
cs.CV cs.AI cs.DB cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The capability to detect boulders on the surface of small bodies is
beneficial for vision-based applications such as hazard detection during
critical operations and navigation. This task is challenging due to the wide
assortment of irregular shapes, the characteristics of the boulders population,
and the rapid variability in the illumination conditions. Moreover, the lack of
publicly available labeled datasets for these applications damps the research
about data-driven algorithms. In this work, the authors provide a statistical
characterization and setup used for the generation of two datasets about
boulders on small bodies that are made publicly available.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 16:39:06 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Pugliatti",
"Mattia",
""
],
[
"Topputo",
"Francesco",
""
]
] |
new_dataset
| 0.999698 |
2210.16261
|
Ian Cosden
|
Ian A. Cosden
|
An RSE Group Model: Operational and Organizational Approaches From
Princeton University's Central Research Software Engineering Group
|
Submitted to IEEE Computing in Science & Engineering (CiSE) Special
Issue on the Future of Research Software Engineers in the US
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Princeton Research Software Engineering Group has grown rapidly since its
inception in late 2016. The group, housed in the central Research Computing
Department, comprised of professional Research Software Engineers (RSEs), works
directly with researchers to create high quality research software to enable
new scientific advances. As the group has matured so has the need for
formalizing operational details and procedures. The RSE group uses an RSE
partnership model, where Research Software Engineers work long-term with a
designated academic department, institute, center, consortium, or individual
principal investigator (PI). This article describes the operation of the
central Princeton RSE group including funding, partner & project selection, and
best practices for defining expectations for a successful partnership with
researchers.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 16:51:31 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Cosden",
"Ian A.",
""
]
] |
new_dataset
| 0.998954 |
2210.16285
|
Muhammad Irfan Yousuf Dr.
|
Muhammad Irfan Yousuf, Izza Anwer, Tanzeela Shakir, Minahil Siddiqui,
Maysoon Shahid
|
Multi-feature Dataset for Windows PE Malware Classification
|
9 Pages, 1 Figure, 5 Tables
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
This paper describes a multi-feature dataset for training machine learning
classifiers for detecting malicious Windows Portable Executable (PE) files. The
dataset includes four feature sets from 18,551 binary samples belonging to five
malware families including Spyware, Ransomware, Downloader, Backdoor and
Generic Malware. The feature sets include the list of DLLs and their functions,
values of different fields of PE Header and Sections. First, we explain the
data collection and creation phase and then we explain how did we label the
samples in it using VirusTotal's services. Finally, we explore the dataset to
describe how this dataset can benefit the researchers for static malware
analysis. The dataset is made public in the hope that it will help inspire
machine learning research for malware detection.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 17:23:21 GMT"
}
] | 2022-10-31T00:00:00 |
[
[
"Yousuf",
"Muhammad Irfan",
""
],
[
"Anwer",
"Izza",
""
],
[
"Shakir",
"Tanzeela",
""
],
[
"Siddiqui",
"Minahil",
""
],
[
"Shahid",
"Maysoon",
""
]
] |
new_dataset
| 0.999774 |
2112.08557
|
Yi Fang
|
Yi Fang, Pingping Chen, Yong Liang Guan, Francis C. M. Lau, Yonghui
Li, Guanrong Chen
|
Protograph Bit-Interleaved Coded Modulation: A Bandwidth-Efficient
Design Paradigm for 6G Wireless Communications
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bit-interleaved coded modulation (BICM) has attracted considerable attention
from the research community in the past three decades, because it can achieve
desirable error performance with relatively low implementation complexity for a
large number of communication and storage systems. By exploiting the iterative
demapping and decoding (ID), the BICM is able to approach capacity limits of
coded modulation over various channels. In recent years, protograph low-density
parity-check (PLDPC) codes and their spatially-coupled (SC) variants have
emerged to be a pragmatic forward-error-correction (FEC) solution for BICM
systems due to their tremendous error-correction capability and simple
structures, and found widespread applications such as deep-space communication,
satellite communication, wireless communication, optical communication, and
data storage. This article offers a comprehensive survey on the
state-of-the-art development of PLDPC-BICM and its innovative SC variants over
a variety of channel models, e.g., additive white Gaussian noise (AWGN)
channels, fading channels, Poisson pulse position modulation (PPM) channels,
and flash-memory channels. Of particular interest is code construction,
constellation shaping, as well as bit-mapper design, where the receiver is
formulated as a serially-concatenated decoding framework consisting of a
soft-decision demapper and a belief-propagation decoder. Finally, several
promising research directions are discussed, which have not been adequately
addressed in the current literature.
|
[
{
"version": "v1",
"created": "Thu, 16 Dec 2021 01:39:53 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 11:40:45 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Fang",
"Yi",
""
],
[
"Chen",
"Pingping",
""
],
[
"Guan",
"Yong Liang",
""
],
[
"Lau",
"Francis C. M.",
""
],
[
"Li",
"Yonghui",
""
],
[
"Chen",
"Guanrong",
""
]
] |
new_dataset
| 0.99337 |
2201.09280
|
Rishiraj Adhikary
|
Rishiraj Adhikary, Dhruvi Lodhavia, Chris Francis, Rohit Patil, Tanmay
Srivastava, Prerna Khanna, Nipun Batra, Joe Breda, Jacob Peplinski, Shwetak
Patel
|
SpiroMask: Measuring Lung Function Using Consumer-Grade Masks
|
Accepted in the ACM Transactions on Computing for Healthcare (HEALTH)
| null | null | null |
cs.HC cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
According to the World Health Organisation (WHO), 235 million people suffer
from respiratory illnesses and four million people die annually due to air
pollution. Regular lung health monitoring can lead to prognoses about
deteriorating lung health conditions. This paper presents our system SpiroMask
that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for
continuous lung health monitoring. We evaluate our approach on 48 participants
(including 14 with lung health issues) and find that we can estimate parameters
such as lung volume and respiration rate within the approved error range by the
American Thoracic Society (ATS). Further, we show that our approach is robust
to sensor placement inside the mask.
|
[
{
"version": "v1",
"created": "Sun, 23 Jan 2022 14:32:38 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Jan 2022 11:09:23 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Jan 2022 09:54:17 GMT"
},
{
"version": "v4",
"created": "Mon, 31 Jan 2022 04:55:01 GMT"
},
{
"version": "v5",
"created": "Wed, 26 Oct 2022 19:47:17 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Adhikary",
"Rishiraj",
""
],
[
"Lodhavia",
"Dhruvi",
""
],
[
"Francis",
"Chris",
""
],
[
"Patil",
"Rohit",
""
],
[
"Srivastava",
"Tanmay",
""
],
[
"Khanna",
"Prerna",
""
],
[
"Batra",
"Nipun",
""
],
[
"Breda",
"Joe",
""
],
[
"Peplinski",
"Jacob",
""
],
[
"Patel",
"Shwetak",
""
]
] |
new_dataset
| 0.999669 |
2203.05437
|
Prasanna Raj Noel Dabre
|
Aman Kumar, Himani Shrotriya, Prachi Sahu, Raj Dabre, Ratish
Puduppully, Anoop Kunchukuttan, Amogh Mishra, Mitesh M. Khapra, Pratyush
Kumar
|
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic
Languages
|
Accepted at EMNLP 2022
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Natural Language Generation (NLG) for non-English languages is hampered by
the scarcity of datasets in these languages. In this paper, we present the
IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic
languages. We focus on five diverse tasks, namely, biography generation using
Wikipedia infoboxes, news headline generation, sentence summarization,
paraphrase generation and, question generation. We describe the created
datasets and use them to benchmark the performance of several monolingual and
multilingual baselines that leverage pre-trained sequence-to-sequence models.
Our results exhibit the strong performance of multilingual language-specific
pre-trained models, and the utility of models trained on our dataset for other
related NLG tasks. Our dataset creation methods can be easily applied to
modest-resource languages as they involve simple steps such as scraping news
articles and Wikipedia infoboxes, light cleaning, and pivoting through machine
translation data. To the best of our knowledge, the IndicNLG Benchmark is the
first NLG benchmark for Indic languages and the most diverse multilingual NLG
dataset, with approximately 8M examples across 5 tasks and 11 languages. The
datasets and models are publicly available at
https://ai4bharat.iitm.ac.in/indicnlg-suite.
|
[
{
"version": "v1",
"created": "Thu, 10 Mar 2022 15:53:58 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 02:33:39 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Kumar",
"Aman",
""
],
[
"Shrotriya",
"Himani",
""
],
[
"Sahu",
"Prachi",
""
],
[
"Dabre",
"Raj",
""
],
[
"Puduppully",
"Ratish",
""
],
[
"Kunchukuttan",
"Anoop",
""
],
[
"Mishra",
"Amogh",
""
],
[
"Khapra",
"Mitesh M.",
""
],
[
"Kumar",
"Pratyush",
""
]
] |
new_dataset
| 0.999771 |
2203.11471
|
Yu Zhan
|
Yu Zhan, Fenghai Li, Renliang Weng, Wongun Choi
|
Ray3D: ray-based 3D human pose estimation for monocular absolute 3D
localization
|
Accepted by CVPR 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute
human pose estimation with calibrated camera. Accurate and generalizable
absolute 3D human pose estimation from monocular 2D pose input is an ill-posed
problem. To address this challenge, we convert the input from pixel space to 3D
normalized rays. This conversion makes our approach robust to camera intrinsic
parameter changes. To deal with the in-the-wild camera extrinsic parameter
variations, Ray3D explicitly takes the camera extrinsic parameters as an input
and jointly models the distribution between the 3D pose rays and camera
extrinsic parameters. This novel network design is the key to the outstanding
generalizability of Ray3D approach. To have a comprehensive understanding of
how the camera intrinsic and extrinsic parameter variations affect the accuracy
of absolute 3D key-point localization, we conduct in-depth systematic
experiments on three single person 3D benchmarks as well as one synthetic
benchmark. These experiments demonstrate that our method significantly
outperforms existing state-of-the-art models. Our code and the synthetic
dataset are available at https://github.com/YxZhxn/Ray3D .
|
[
{
"version": "v1",
"created": "Tue, 22 Mar 2022 05:42:31 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Mar 2022 06:29:45 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Oct 2022 06:40:16 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Zhan",
"Yu",
""
],
[
"Li",
"Fenghai",
""
],
[
"Weng",
"Renliang",
""
],
[
"Choi",
"Wongun",
""
]
] |
new_dataset
| 0.989909 |
2204.05070
|
Karolos Nikitaras
|
Karolos Nikitaras, Georgios Vamvoukakis, Nikolaos Ellinas,
Konstantinos Klapsas, Konstantinos Markopoulos, Spyros Raptis, June Sig Sung,
Gunu Jho, Aimilios Chalamandaris, Pirros Tsiakoulis
|
Fine-grained Noise Control for Multispeaker Speech Synthesis
|
Accepted to INTERSPEECH 2022
| null |
10.21437/Interspeech.2022-10765
| null |
cs.SD cs.CL cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A text-to-speech (TTS) model typically factorizes speech attributes such as
content, speaker and prosody into disentangled representations.Recent works aim
to additionally model the acoustic conditions explicitly, in order to
disentangle the primary speech factors, i.e. linguistic content, prosody and
timbre from any residual factors, such as recording conditions and background
noise.This paper proposes unsupervised, interpretable and fine-grained noise
and prosody modeling. We incorporate adversarial training, representation
bottleneck and utterance-to-frame modeling in order to learn frame-level noise
representations. To the same end, we perform fine-grained prosody modeling via
a Fully Hierarchical Variational AutoEncoder (FVAE) which additionally results
in more expressive speech synthesis.
|
[
{
"version": "v1",
"created": "Mon, 11 Apr 2022 13:13:55 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 16:26:24 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Nikitaras",
"Karolos",
""
],
[
"Vamvoukakis",
"Georgios",
""
],
[
"Ellinas",
"Nikolaos",
""
],
[
"Klapsas",
"Konstantinos",
""
],
[
"Markopoulos",
"Konstantinos",
""
],
[
"Raptis",
"Spyros",
""
],
[
"Sung",
"June Sig",
""
],
[
"Jho",
"Gunu",
""
],
[
"Chalamandaris",
"Aimilios",
""
],
[
"Tsiakoulis",
"Pirros",
""
]
] |
new_dataset
| 0.998966 |
2205.14459
|
Shashank Goel
|
Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa
Vinay, Aditya Grover
|
CyCLIP: Cyclic Contrastive Language-Image Pretraining
|
19 pages, 13 tables, 6 figures, Oral at NeuRIPS 2022
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent advances in contrastive representation learning over paired image-text
data have led to models such as CLIP that achieve state-of-the-art performance
for zero-shot classification and distributional robustness. Such models
typically require joint reasoning in the image and text representation spaces
for downstream inference tasks. Contrary to prior beliefs, we demonstrate that
the image and text representations learned via a standard contrastive objective
are not interchangeable and can lead to inconsistent downstream predictions. To
mitigate this issue, we formalize consistency and propose CyCLIP, a framework
for contrastive representation learning that explicitly optimizes for the
learned representations to be geometrically consistent in the image and text
space. In particular, we show that consistent representations can be learned by
explicitly symmetrizing (a) the similarity between the two mismatched
image-text pairs (cross-modal consistency); and (b) the similarity between the
image-image pair and the text-text pair (in-modal consistency). Empirically, we
show that the improved consistency in CyCLIP translates to significant gains
over CLIP, with gains ranging from 10%-24% for zero-shot classification
accuracy on standard benchmarks (CIFAR-10, CIFAR-100, ImageNet1K) and 10%-27%
for robustness to various natural distribution shifts. The code is available at
https://github.com/goel-shashank/CyCLIP.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 15:31:17 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Oct 2022 18:30:33 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Goel",
"Shashank",
""
],
[
"Bansal",
"Hritik",
""
],
[
"Bhatia",
"Sumit",
""
],
[
"Rossi",
"Ryan A.",
""
],
[
"Vinay",
"Vishwa",
""
],
[
"Grover",
"Aditya",
""
]
] |
new_dataset
| 0.95749 |
2208.05004
|
Zuher Jahshan
|
Zuher Jahshan, Can Alkan and Leonid Yavits
|
CoViT: Real-time phylogenetics for the SARS-CoV-2 pandemic using Vision
Transformers
|
11 pages, 4 figures, 2 tables
| null | null | null |
cs.LG q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Real-time viral genome detection, taxonomic classification and phylogenetic
analysis are critical for efficient tracking and control of viral pandemics
such as Covid-19. However, the unprecedented and still growing amounts of viral
genome data create a computational bottleneck, which effectively prevents the
real-time pandemic tracking. For genomic tracing to work effectively, each new
viral genome sequence must be placed in its pangenomic context. Re-inferring
the full phylogeny of SARS-CoV-2, with datasets containing millions of samples,
is prohibitively slow even using powerful computational resources. We are
attempting to alleviate the computational bottleneck by modifying and applying
Vision Transformer, a recently developed neural network model for image
recognition, to taxonomic classification and placement of viral genomes, such
as SARS-CoV-2. Our solution, CoViT, places SARS-CoV-2 genome accessions onto
SARS-CoV-2 phylogenetic tree with the accuracy of 94.2%. Since CoViT is a
classification neural network, it provides more than one likely placement.
Specifically, one of the two most likely placements suggested by CoViT is
correct with the probability of 97.9%. The probability of the correct placement
to be found among the five most likely placements generated by CoViT is 99.8%.
The placement time is 0.055s per individual genome running on NVIDIAs GeForce
RTX 2080 Ti GPU. We make CoViT available to research community through GitHub:
https://github.com/zuherJahshan/covit.
|
[
{
"version": "v1",
"created": "Tue, 9 Aug 2022 19:13:41 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 09:14:44 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Jahshan",
"Zuher",
""
],
[
"Alkan",
"Can",
""
],
[
"Yavits",
"Leonid",
""
]
] |
new_dataset
| 0.998668 |
2208.10607
|
Jonathan Ventura
|
Jonathan Ventura, Camille Pawlak, Milo Honsberger, Cameron Gonsalves,
Julian Rice, Natalie L.R. Love, Skyler Han, Viet Nguyen, Keilana Sugano,
Jacqueline Doremus, G. Andrew Fricker, Jenn Yost, Matt Ritter
|
Individual Tree Detection in Large-Scale Urban Environments using
High-Resolution Multispectral Imagery
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a novel deep learning method for detection of individual trees
in urban environments using high-resolution multispectral aerial imagery. We
use a convolutional neural network to regress a confidence map indicating the
locations of individual trees, which are localized using a peak finding
algorithm. Our method provides complete spatial coverage by detecting trees in
both public and private spaces, and can scale to very large areas. We performed
a thorough evaluation of our method, supported by a new dataset of over 1,500
images and almost 100,000 tree annotations, covering eight cities, six climate
zones, and three image capture years. We trained our model on data from
Southern California, and achieved a precision of 73.6% and recall of 73.3%
using test data from this region. We generally observed similar precision and
slightly lower recall when extrapolating to other California climate zones and
image capture dates. We used our method to produce a map of trees in the entire
urban forest of California, and estimated the total number of urban trees in
California to be about 43.5 million. Our study indicates the potential for deep
learning methods to support future urban forestry studies at unprecedented
scales.
|
[
{
"version": "v1",
"created": "Mon, 22 Aug 2022 21:26:57 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Aug 2022 17:45:38 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Oct 2022 04:51:55 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Ventura",
"Jonathan",
""
],
[
"Pawlak",
"Camille",
""
],
[
"Honsberger",
"Milo",
""
],
[
"Gonsalves",
"Cameron",
""
],
[
"Rice",
"Julian",
""
],
[
"Love",
"Natalie L. R.",
""
],
[
"Han",
"Skyler",
""
],
[
"Nguyen",
"Viet",
""
],
[
"Sugano",
"Keilana",
""
],
[
"Doremus",
"Jacqueline",
""
],
[
"Fricker",
"G. Andrew",
""
],
[
"Yost",
"Jenn",
""
],
[
"Ritter",
"Matt",
""
]
] |
new_dataset
| 0.995405 |
2210.10335
|
Namhyuk Ahn
|
Jihye Back, Seungkwon Kim, Namhyuk Ahn
|
WebtoonMe: A Data-Centric Approach for Full-Body Portrait Stylization
|
SIGGRAPH Asia 2022 Technical Communications
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Full-body portrait stylization, which aims to translate portrait photography
into a cartoon style, has drawn attention recently. However, most methods have
focused only on converting face regions, restraining the feasibility of use in
real-world applications. A recently proposed two-stage method expands the
rendering area to full bodies, but the outputs are less plausible and fail to
achieve quality robustness of non-face regions. Furthermore, they cannot
reflect diverse skin tones. In this study, we propose a data-centric solution
to build a production-level full-body portrait stylization system. Based on the
two-stage scheme, we construct a novel and advanced dataset preparation
paradigm that can effectively resolve the aforementioned problems. Experiments
reveal that with our pipeline, high-quality portrait stylization can be
achieved without additional losses or architectural changes.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 07:09:03 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 05:01:19 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Back",
"Jihye",
""
],
[
"Kim",
"Seungkwon",
""
],
[
"Ahn",
"Namhyuk",
""
]
] |
new_dataset
| 0.996535 |
2210.11674
|
Enting Ying
|
Enting Ying and Tianyang Xiong and Shihui Guo and Ming Qiu and Yipeng
Qin and Hongbo Fu
|
WristSketcher: Creating Dynamic Sketches in AR with a Sensing Wristband
| null | null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Restricted by the limited interaction area of native AR glasses (e.g., touch
bars), it is challenging to create sketches in AR glasses. Recent works have
attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures
to expand the interactive spaces and can work as the 2D/3D sketching input
interfaces for AR glasses. Between them, mobile devices allow for accurate
sketching but are often heavy to carry, while sketching with bare hands is
zero-burden but can be inaccurate due to arm instability. In addition, mid-air
bare-hand sketching can easily lead to social misunderstandings and its
prolonged use can cause arm fatigue. As a new attempt, in this work, we present
WristSketcher, a new AR system based on a flexible sensing wristband for
creating 2D dynamic sketches, featuring an almost zero-burden authoring model
for accurate and comfortable sketch creation in real-world scenarios.
Specifically, we have streamlined the interaction space from the mid-air to the
surface of a lightweight sensing wristband, and implemented AR sketching and
associated interaction commands by developing a gesture recognition method
based on the sensing pressure points on the wristband. The set of interactive
gestures used by our WristSketcher is determined by a heuristic study on user
preferences. Moreover, we endow our WristSketcher with the ability of animation
creation, allowing it to create dynamic and expressive sketches. Experimental
results demonstrate that our WristSketcher i) faithfully recognizes users'
gesture interactions with a high accuracy of 96.0%; ii) achieves higher
sketching accuracy than Freehand sketching; iii) achieves high user
satisfaction in ease of use, usability and functionality; and iv) shows
innovation potentials in art creation, memory aids, and entertainment
applications.
|
[
{
"version": "v1",
"created": "Fri, 21 Oct 2022 02:00:41 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 01:26:09 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Ying",
"Enting",
""
],
[
"Xiong",
"Tianyang",
""
],
[
"Guo",
"Shihui",
""
],
[
"Qiu",
"Ming",
""
],
[
"Qin",
"Yipeng",
""
],
[
"Fu",
"Hongbo",
""
]
] |
new_dataset
| 0.999553 |
2210.14320
|
Rini Jasmine Gladstone
|
Rini J. Gladstone, Mohammad A. Nabian, Hadi Meidani
|
FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
|
6 pages, 3 figures, Selected for ML4PS workshop at NeurIPS 2022
| null | null | null |
cs.LG cs.NA math.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present FO-PINNs, physics-informed neural networks that are trained using
the first-order formulation of the Partial Differential Equation (PDE) losses.
We show that FO-PINNs offer significantly higher accuracy in solving
parameterized systems compared to traditional PINNs, and reduce
time-per-iteration by removing the extra backpropagations needed to compute the
second or higher-order derivatives. Additionally, unlike standard PINNs,
FO-PINNs can be used with exact imposition of boundary conditions using
approximate distance functions, and can be trained using Automatic Mixed
Precision (AMP) to further speed up the training. Through two Helmholtz and
Navier-Stokes examples, we demonstrate the advantages of FO-PINNs over
traditional PINNs in terms of accuracy and training speedup.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 20:25:33 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Gladstone",
"Rini J.",
""
],
[
"Nabian",
"Mohammad A.",
""
],
[
"Meidani",
"Hadi",
""
]
] |
new_dataset
| 0.995654 |
2210.14461
|
Dhruv Makwana
|
Onkar Susladkar, Dhruv Makwana, Gayatri Deshmukh, Sparsh Mittal, Sai
Chandra Teja R, Rekha Singhal
|
TPFNet: A Novel Text In-painting Transformer for Text Removal
|
10 pages, 5 figures, 5 tables, Neurips Proceedings
| null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Text erasure from an image is helpful for various tasks such as image editing
and privacy preservation. In this paper, we present TPFNet, a novel one-stage
(end-toend) network for text removal from images. Our network has two parts:
feature synthesis and image generation. Since noise can be more effectively
removed from low-resolution images, part 1 operates on low-resolution images.
The output of part 1 is a low-resolution text-free image. Part 2 uses the
features learned in part 1 to predict a high-resolution text-free image. In
part 1, we use "pyramidal vision transformer" (PVT) as the encoder. Further, we
use a novel multi-headed decoder that generates a high-pass filtered image and
a segmentation map, in addition to a text-free image. The segmentation branch
helps locate the text precisely, and the high-pass branch helps in learning the
image structure. To precisely locate the text, TPFNet employs an adversarial
loss that is conditional on the segmentation map rather than the input image.
On Oxford, SCUT, and SCUT-EnsText datasets, our network outperforms recently
proposed networks on nearly all the metrics. For example, on SCUT-EnsText
dataset, TPFNet has a PSNR (higher is better) of 39.0 and text-detection
precision (lower is better) of 21.1, compared to the best previous technique,
which has a PSNR of 32.3 and precision of 53.2. The source code can be obtained
from https://github.com/CandleLabAI/TPFNet
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 04:16:50 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 14:14:55 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Susladkar",
"Onkar",
""
],
[
"Makwana",
"Dhruv",
""
],
[
"Deshmukh",
"Gayatri",
""
],
[
"Mittal",
"Sparsh",
""
],
[
"R",
"Sai Chandra Teja",
""
],
[
"Singhal",
"Rekha",
""
]
] |
new_dataset
| 0.99973 |
2210.14997
|
Manthan Patel
|
Manthan Patel, Gabriel Waibel, Shehryar Khattak, Marco Hutter
|
LiDAR-guided object search and detection in Subterranean Environments
|
6 pages, 5 Figures, 2 Tables, conference: IEEE International
Symposium on Safety, Security and Rescue Robotics (SSRR-2022), Seville, Spain
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Detecting objects of interest, such as human survivors, safety equipment, and
structure access points, is critical to any search-and-rescue operation. Robots
deployed for such time-sensitive efforts rely on their onboard sensors to
perform their designated tasks. However, as disaster response operations are
predominantly conducted under perceptually degraded conditions, commonly
utilized sensors such as visual cameras and LiDARs suffer in terms of
performance degradation. In response, this work presents a method that utilizes
the complementary nature of vision and depth sensors to leverage multi-modal
information to aid object detection at longer distances. In particular, depth
and intensity values from sparse LiDAR returns are used to generate proposals
for objects present in the environment. These proposals are then utilized by a
Pan-Tilt-Zoom (PTZ) camera system to perform a directed search by adjusting its
pose and zoom level for performing object detection and classification in
difficult environments. The proposed work has been thoroughly verified using an
ANYmal quadruped robot in underground settings and on datasets collected during
the DARPA Subterranean Challenge finals.
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 19:38:19 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Patel",
"Manthan",
""
],
[
"Waibel",
"Gabriel",
""
],
[
"Khattak",
"Shehryar",
""
],
[
"Hutter",
"Marco",
""
]
] |
new_dataset
| 0.999268 |
2210.15040
|
Dan Casas
|
Andr\'es Casado-Elvira and Marc Comino Trinidad and Dan Casas
|
PERGAMO: Personalized 3D Garments from Monocular Video
|
Published at Computer Graphics Forum (Proc. of ACM/SIGGRAPH SCA),
2022. Project website http://mslab.es/projects/PERGAMO/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Clothing plays a fundamental role in digital humans. Current approaches to
animate 3D garments are mostly based on realistic physics simulation, however,
they typically suffer from two main issues: high computational run-time cost,
which hinders their development; and simulation-to-real gap, which impedes the
synthesis of specific real-world cloth samples. To circumvent both issues we
propose PERGAMO, a data-driven approach to learn a deformable model for 3D
garments from monocular images. To this end, we first introduce a novel method
to reconstruct the 3D geometry of garments from a single image, and use it to
build a dataset of clothing from monocular videos. We use these 3D
reconstructions to train a regression model that accurately predicts how the
garment deforms as a function of the underlying body pose. We show that our
method is capable of producing garment animations that match the real-world
behaviour, and generalizes to unseen body motions extracted from motion capture
dataset.
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 21:15:54 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Casado-Elvira",
"Andrés",
""
],
[
"Trinidad",
"Marc Comino",
""
],
[
"Casas",
"Dan",
""
]
] |
new_dataset
| 0.996633 |
2210.15050
|
Hyunwook Lee
|
Hyunwook Lee, Chunggi Lee, Hongkyu Lim, Sungahn Ko
|
TILDE-Q: A Transformation Invariant Loss Function for Time-Series
Forecasting
|
9 pages paper, 2 pages references, and 7 pages appendix. Submitted as
conference paper to ICLR 2023
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Time-series forecasting has caught increasing attention in the AI research
field due to its importance in solving real-world problems across different
domains, such as energy, weather, traffic, and economy. As shown in various
types of data, it has been a must-see issue to deal with drastic changes,
temporal patterns, and shapes in sequential data that previous models are weak
in prediction. This is because most cases in time-series forecasting aim to
minimize $L_p$ norm distances as loss functions, such as mean absolute error
(MAE) or mean square error (MSE). These loss functions are vulnerable to not
only considering temporal dynamics modeling but also capturing the shape of
signals. In addition, these functions often make models misbehave and return
uncorrelated results to the original time-series. To become an effective loss
function, it has to be invariant to the set of distortions between two
time-series data instead of just comparing exact values. In this paper, we
propose a novel loss function, called TILDE-Q (Transformation Invariant Loss
function with Distance EQuilibrium), that not only considers the distortions in
amplitude and phase but also allows models to capture the shape of time-series
sequences. In addition, TILDE-Q supports modeling periodic and non-periodic
temporal dynamics at the same time. We evaluate the effectiveness of TILDE-Q by
conducting extensive experiments with respect to periodic and non-periodic
conditions of data, from naive models to state-of-the-art models. The
experiment results indicate that the models trained with TILDE-Q outperform
those trained with other training metrics (e.g., MSE, dynamic time warping
(DTW), temporal distortion index (TDI), and longest common subsequence (LCSS)).
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 21:32:20 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Lee",
"Hyunwook",
""
],
[
"Lee",
"Chunggi",
""
],
[
"Lim",
"Hongkyu",
""
],
[
"Ko",
"Sungahn",
""
]
] |
new_dataset
| 0.990552 |
2210.15085
|
Mohammadhadi Mohandes
|
Mohammadhadi Mohandes, Behnam Moradi, Kamal Gupta, Mehran Mehrandezh
|
Robot to Human Object Handover using Vision and Joint Torque Sensor
Modalities
|
Note: This paper is submitted to RITA 2022 conference and waiting for
results
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a robot-to-human object handover algorithm and implement it on a
7-DOF arm equipped with a 3-finger mechanical hand. The system performs a fully
autonomous and robust object handover to a human receiver in real-time. Our
algorithm relies on two complementary sensor modalities: joint torque sensors
on the arm and an eye-in-hand RGB-D camera for sensor feedback. Our approach is
entirely implicit, i.e., there is no explicit communication between the robot
and the human receiver. Information obtained via the aforementioned sensor
modalities is used as inputs to their related deep neural networks. While the
torque sensor network detects the human receiver's "intention" such as: pull,
hold, or bump, the vision sensor network detects if the receiver's fingers have
wrapped around the object. Networks' outputs are then fused, based on which a
decision is made to either release the object or not. Despite substantive
challenges in sensor feedback synchronization, object, and human hand
detection, our system achieves robust robot-to-human handover with 98\%
accuracy in our preliminary real experiments using human receivers.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 00:11:34 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Mohandes",
"Mohammadhadi",
""
],
[
"Moradi",
"Behnam",
""
],
[
"Gupta",
"Kamal",
""
],
[
"Mehrandezh",
"Mehran",
""
]
] |
new_dataset
| 0.991541 |
2210.15104
|
Piyush Behre
|
Piyush Behre, Sharman Tan, Amy Shah, Harini Kesavamoorthy, Shuangyu
Chang, Fei Zuo, Chris Basoglu, Sayan Pathak
|
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and
Punctuation model evaluation and selection
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Punctuation and Segmentation are key to readability in Automatic Speech
Recognition (ASR), often evaluated using F1 scores that require high-quality
human transcripts and do not reflect readability well. Human evaluation is
expensive, time-consuming, and suffers from large inter-observer variability,
especially in conversational speech devoid of strict grammatical structures.
Large pre-trained models capture a notion of grammatical structure. We present
TRScore, a novel readability measure using the GPT model to evaluate different
segmentation and punctuation systems. We validate our approach with human
experts. Additionally, our approach enables quantitative assessment of text
post-processing techniques such as capitalization, inverse text normalization
(ITN), and disfluency on overall readability, which traditional word error rate
(WER) and slot error rate (SER) metrics fail to capture. TRScore is strongly
correlated to traditional F1 and human readability scores, with Pearson's
correlation coefficients of 0.67 and 0.98, respectively. It also eliminates the
need for human transcriptions for model selection.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 01:11:32 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Behre",
"Piyush",
""
],
[
"Tan",
"Sharman",
""
],
[
"Shah",
"Amy",
""
],
[
"Kesavamoorthy",
"Harini",
""
],
[
"Chang",
"Shuangyu",
""
],
[
"Zuo",
"Fei",
""
],
[
"Basoglu",
"Chris",
""
],
[
"Pathak",
"Sayan",
""
]
] |
new_dataset
| 0.98037 |
2210.15126
|
Jianxiang Zhou
|
Cunxi Dai, Xiaohan Liu, Jianxiang Zhou, Zhengtao Liu, Zhenzhong Jia
|
SWheg: A Wheel-Leg Transformable Robot With Minimalist Actuator
Realization
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This article presents the design, implementation, and performance evaluation
of SWheg, a novel modular wheel-leg transformable robot family with minimalist
actuator realization. SWheg takes advantage of both wheeled and legged
locomotion by seamlessly integrating them on a single platform. In contrast to
other designs that use multiple actuators, SWheg uses only one actuator to
drive the transformation of all the wheel-leg modules in sync. This means an
N-legged SWheg robot requires only N+1 actuators, which can significantly
reduce the cost and malfunction rate of the platform. The tendon-driven
wheel-leg transformation mechanism based on a four-bar linkage can perform fast
morphology transitions between wheels and legs. We validated the design
principle with two SWheg robots with four and six wheel-leg modules separately,
namely Quadrupedal SWheg and Hexapod SWheg. The design process, mechatronics
infrastructure, and the gait behavioral development of both platforms were
discussed. The performance of the robot was evaluated in various scenarios,
including driving and turning in wheeled mode, step crossing, irregular terrain
passing, and stair climbing in legged mode. The comparison between these two
platforms was also discussed.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 02:18:53 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Dai",
"Cunxi",
""
],
[
"Liu",
"Xiaohan",
""
],
[
"Zhou",
"Jianxiang",
""
],
[
"Liu",
"Zhengtao",
""
],
[
"Jia",
"Zhenzhong",
""
]
] |
new_dataset
| 0.999531 |
2210.15128
|
Yongwei Miao
|
Chen Bao, Xudong Zhang, Jiazhou Chen, Yongwei Miao
|
MMFL-Net: Multi-scale and Multi-granularity Feature Learning for
Cross-domain Fashion Retrieval
|
27 pages, 12 figures, Published by <Multimedia Tools and
Applications>
|
Multimedia Tools and Applications(2022)1-27
|
10.1007/s11042-022-13648-8
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Instance-level image retrieval in fashion is a challenging issue owing to its
increasing importance in real-scenario visual fashion search. Cross-domain
fashion retrieval aims to match the unconstrained customer images as queries
for photographs provided by retailers; however, it is a difficult task due to a
wide range of consumer-to-shop (C2S) domain discrepancies and also considering
that clothing image is vulnerable to various non-rigid deformations. To this
end, we propose a novel multi-scale and multi-granularity feature learning
network (MMFL-Net), which can jointly learn global-local aggregation feature
representations of clothing images in a unified framework, aiming to train a
cross-domain model for C2S fashion visual similarity. First, a new
semantic-spatial feature fusion part is designed to bridge the semantic-spatial
gap by applying top-down and bottom-up bidirectional multi-scale feature
fusion. Next, a multi-branch deep network architecture is introduced to capture
global salient, part-informed, and local detailed information, and extracting
robust and discrimination feature embedding by integrating the similarity
learning of coarse-to-fine embedding with the multiple granularities. Finally,
the improved trihard loss, center loss, and multi-task classification loss are
adopted for our MMFL-Net, which can jointly optimize intra-class and
inter-class distance and thus explicitly improve intra-class compactness and
inter-class discriminability between its visual representations for feature
learning. Furthermore, our proposed model also combines the multi-task
attribute recognition and classification module with multi-label semantic
attributes and product ID labels. Experimental results demonstrate that our
proposed MMFL-Net achieves significant improvement over the state-of-the-art
methods on the two datasets, DeepFashion-C2S and Street2Shop.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 02:25:52 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Bao",
"Chen",
""
],
[
"Zhang",
"Xudong",
""
],
[
"Chen",
"Jiazhou",
""
],
[
"Miao",
"Yongwei",
""
]
] |
new_dataset
| 0.996516 |
2210.15136
|
Rihao Chang
|
Weizhi Nie, Rihao Chang, Tong Hao, Anan Liu
|
3D Shape Knowledge Graph for Cross-domain and Cross-modal 3D Shape
Retrieval
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the development of 3D modeling and fabrication, 3D shape retrieval has
become a hot topic. In recent years, several strategies have been put forth to
address this retrieval issue. However, it is difficult for them to handle
cross-modal 3D shape retrieval because of the natural differences between
modalities. In this paper, we propose an innovative concept, namely, geometric
words, which is regarded as the basic element to represent any 3D or 2D entity
by combination, and assisted by which, we can simultaneously handle
cross-domain or cross-modal retrieval problems. First, to construct the
knowledge graph, we utilize the geometric word as the node, and then use the
category of the 3D shape as well as the attribute of the geometry to bridge the
nodes. Second, based on the knowledge graph, we provide a unique way for
learning each entity's embedding. Finally, we propose an effective similarity
measure to handle the cross-domain and cross-modal 3D shape retrieval.
Specifically, every 3D or 2D entity could locate its geometric terms in the 3D
knowledge graph, which serve as a link between cross-domain and cross-modal
data. Thus, our approach can achieve the cross-domain and cross-modal 3D shape
retrieval at the same time. We evaluated our proposed method on the ModelNet40
dataset and ShapeNetCore55 dataset for both the 3D shape retrieval task and
cross-domain 3D shape retrieval task. The classic cross-modal dataset (MI3DOR)
is utilized to evaluate cross-modal 3D shape retrieval. Experimental results
and comparisons with state-of-the-art methods illustrate the superiority of our
approach.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 02:51:24 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Nie",
"Weizhi",
""
],
[
"Chang",
"Rihao",
""
],
[
"Hao",
"Tong",
""
],
[
"Liu",
"Anan",
""
]
] |
new_dataset
| 0.95615 |
2210.15234
|
Jamolbek Mattiev Dr
|
Maksud Sharipov, Jamolbek Mattiev, Jasur Sobirov, Rustam Baltayev
|
Creating a morphological and syntactic tagged corpus for the Uzbek
language
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Nowadays, creation of the tagged corpora is becoming one of the most
important tasks of Natural Language Processing (NLP). There are not enough
tagged corpora to build machine learning models for the low-resource Uzbek
language. In this paper, we tried to fill that gap by developing a novel Part
Of Speech (POS) and syntactic tagset for creating the syntactic and
morphologically tagged corpus of the Uzbek language. This work also includes
detailed description and presentation of a web-based application to work on a
tagging as well. Based on the developed annotation tool and the software, we
share our experience results of the first stage of the tagged corpus creation
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 07:44:12 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Sharipov",
"Maksud",
""
],
[
"Mattiev",
"Jamolbek",
""
],
[
"Sobirov",
"Jasur",
""
],
[
"Baltayev",
"Rustam",
""
]
] |
new_dataset
| 0.999343 |
2210.15316
|
Gopi Krishna Erabati
|
Gopi Krishna Erabati and Helder Araujo
|
MSF3DDETR: Multi-Sensor Fusion 3D Detection Transformer for Autonomous
Driving
|
Accepted at the ICPR 2022 Workshop DLVDR2022
| null | null | null |
cs.CV cs.LG cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
3D object detection is a significant task for autonomous driving. Recently
with the progress of vision transformers, the 2D object detection problem is
being treated with the set-to-set loss. Inspired by these approaches on 2D
object detection and an approach for multi-view 3D object detection DETR3D, we
propose MSF3DDETR: Multi-Sensor Fusion 3D Detection Transformer architecture to
fuse image and LiDAR features to improve the detection accuracy. Our end-to-end
single-stage, anchor-free and NMS-free network takes in multi-view images and
LiDAR point clouds and predicts 3D bounding boxes. Firstly, we link the object
queries learnt from data to the image and LiDAR features using a novel
MSF3DDETR cross-attention block. Secondly, the object queries interacts with
each other in multi-head self-attention block. Finally, MSF3DDETR block is
repeated for $L$ number of times to refine the object queries. The MSF3DDETR
network is trained end-to-end on the nuScenes dataset using Hungarian algorithm
based bipartite matching and set-to-set loss inspired by DETR. We present both
quantitative and qualitative results which are competitive to the
state-of-the-art approaches.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 10:55:15 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Erabati",
"Gopi Krishna",
""
],
[
"Araujo",
"Helder",
""
]
] |
new_dataset
| 0.99902 |
2210.15360
|
Rui Liu
|
Yifan Hu, Rui Liu, Guanglai Gao, Haizhou Li
|
FCTalker: Fine and Coarse Grained Context Modeling for Expressive
Conversational Speech Synthesis
|
5 pages, 4 figures, 1 table. Submitted to ICASSP 2023. We release the
source code at: https://github.com/walker-hyf/FCTalker
| null | null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Conversational Text-to-Speech (TTS) aims to synthesis an utterance with the
right linguistic and affective prosody in a conversational context. The
correlation between the current utterance and the dialogue history at the
utterance level was used to improve the expressiveness of synthesized speech.
However, the fine-grained information in the dialogue history at the word level
also has an important impact on the prosodic expression of an utterance, which
has not been well studied in the prior work. Therefore, we propose a novel
expressive conversational TTS model, termed as FCTalker, that learn the fine
and coarse grained context dependency at the same time during speech
generation. Specifically, the FCTalker includes fine and coarse grained
encoders to exploit the word and utterance-level context dependency. To model
the word-level dependencies between an utterance and its dialogue history, the
fine-grained dialogue encoder is built on top of a dialogue BERT model. The
experimental results show that the proposed method outperforms all baselines
and generates more expressive speech that is contextually appropriate. We
release the source code at: https://github.com/walker-hyf/FCTalker.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 12:20:20 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Hu",
"Yifan",
""
],
[
"Liu",
"Rui",
""
],
[
"Gao",
"Guanglai",
""
],
[
"Li",
"Haizhou",
""
]
] |
new_dataset
| 0.992227 |
2210.15364
|
Rui Liu
|
Rui Liu, Haolin Zuo, De Hu, Guanglai Gao, Haizhou Li
|
Explicit Intensity Control for Accented Text-to-speech
|
5 pages, 3 figures. Submitted to ICASSP 2023. arXiv admin note: text
overlap with arXiv:2209.10804
| null | null | null |
cs.SD cs.AI eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Accented text-to-speech (TTS) synthesis seeks to generate speech with an
accent (L2) as a variant of the standard version (L1). How to control the
intensity of accent in the process of TTS is a very interesting research
direction, and has attracted more and more attention. Recent work design a
speaker-adversarial loss to disentangle the speaker and accent information, and
then adjust the loss weight to control the accent intensity. However, such a
control method lacks interpretability, and there is no direct correlation
between the controlling factor and natural accent intensity. To this end, this
paper propose a new intuitive and explicit accent intensity control scheme for
accented TTS. Specifically, we first extract the posterior probability, called
as ``goodness of pronunciation (GoP)'' from the L1 speech recognition model to
quantify the phoneme accent intensity for accented speech, then design a
FastSpeech2 based TTS model, named Ai-TTS, to take the accent intensity
expression into account during speech generation. Experiments show that the our
method outperforms the baseline model in terms of accent rendering and
intensity control.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 12:23:41 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Liu",
"Rui",
""
],
[
"Zuo",
"Haolin",
""
],
[
"Hu",
"De",
""
],
[
"Gao",
"Guanglai",
""
],
[
"Li",
"Haizhou",
""
]
] |
new_dataset
| 0.953199 |
2210.15365
|
Gopi Krishna Erabati
|
Gopi Krishna Erabati and Helder Araujo
|
Li3DeTr: A LiDAR based 3D Detection Transformer
|
Accepted at the IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV) 2023
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Inspired by recent advances in vision transformers for object detection, we
propose Li3DeTr, an end-to-end LiDAR based 3D Detection Transformer for
autonomous driving, that inputs LiDAR point clouds and regresses 3D bounding
boxes. The LiDAR local and global features are encoded using sparse convolution
and multi-scale deformable attention respectively. In the decoder head,
firstly, in the novel Li3DeTr cross-attention block, we link the LiDAR global
features to 3D predictions leveraging the sparse set of object queries learnt
from the data. Secondly, the object query interactions are formulated using
multi-head self-attention. Finally, the decoder layer is repeated $L_{dec}$
number of times to refine the object queries. Inspired by DETR, we employ
set-to-set loss to train the Li3DeTr network. Without bells and whistles, the
Li3DeTr network achieves 61.3% mAP and 67.6% NDS surpassing the
state-of-the-art methods with non-maximum suppression (NMS) on the nuScenes
dataset and it also achieves competitive performance on the KITTI dataset. We
also employ knowledge distillation (KD) using a teacher and student model that
slightly improves the performance of our network.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 12:23:54 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Erabati",
"Gopi Krishna",
""
],
[
"Araujo",
"Helder",
""
]
] |
new_dataset
| 0.999246 |
2210.15386
|
Kwanghee Choi
|
Kwanghee Choi, Eun Jung Yeo
|
Opening the Black Box of wav2vec Feature Encoder
| null | null | null | null |
cs.SD cs.CL cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Self-supervised models, namely, wav2vec and its variants, have shown
promising results in various downstream tasks in the speech domain. However,
their inner workings are poorly understood, calling for in-depth analyses on
what the model learns. In this paper, we concentrate on the convolutional
feature encoder where its latent space is often speculated to represent
discrete acoustic units. To analyze the embedding space in a reductive manner,
we feed the synthesized audio signals, which is the summation of simple sine
waves. Through extensive experiments, we conclude that various information is
embedded inside the feature encoder representations: (1) fundamental frequency,
(2) formants, and (3) amplitude, packed with (4) sufficient temporal detail.
Further, the information incorporated inside the latent representations is
analogous to spectrograms but with a fundamental difference: latent
representations construct a metric space so that closer representations imply
acoustic similarity.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 12:47:35 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Choi",
"Kwanghee",
""
],
[
"Yeo",
"Eun Jung",
""
]
] |
new_dataset
| 0.988115 |
2210.15406
|
Sabah Al-Fedaghi Dr.
|
Sabah Al-Fedaghi
|
Lupascian Non-Negativity Applied to Conceptual Modeling: Alternating
Static Potentiality and Dynamic Actuality
|
11 pages, 21 figures
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
In software engineering, conceptual modeling focuses on creating
representations of the world that are as faithful and rich as possible, with
the aim of guiding the development of software systems. In contrast, in the
computing realm, the notion of ontology has been characterized as being closely
related to conceptual modeling and is often viewed as a specification of a
conceptualization. Accordingly, conceptual modeling and ontology engineering
now address the same problem of representing the world in a suitable fashion. A
high-level ontology provides a means to describe concepts and their
interactions with each other and to capture structural and behavioral features
in the intended domain. This paper aims to analyze ontological concepts and
semantics of modeling notations to provide a common understanding among
software engineers. An important issue in this context concerns the question of
whether the modeled world might be stratified into ontological levels. We
introduce an abstract system of two-level domain ontology to be used as a
foundation for conceptual models. We study the two levels of staticity and
dynamics in the context of the thinging machine (TM) model using the notions of
potentiality and actuality that the Franco-Romanian philosopher Stephane
Lupasco developed in logic. He provided a quasi-universal rejection of
contradiction where every event was always associated with a no event, such
that the actualization of an event entails the potentialization of a no event
and vice versa without either ever disappearing completely. This approach is
illustrated by re-modeling UML state machines in TM modeling. The results
strengthen the semantics of a static versus dynamic levels in conceptual
modeling and sharpen the notion of events as a phenomenon without negativity
alternating between the two levels of dynamics and staticity.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 13:13:07 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Al-Fedaghi",
"Sabah",
""
]
] |
new_dataset
| 0.986729 |
2210.15421
|
Diego Ulisse Pizzagalli
|
Diego Ulisse Pizzagalli, Rolf Krause
|
AnyDijkstra, an algorithm to compute shortest paths on images with
anytime properties
|
7 pages, 4 figures
| null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Images conveniently capture the result of physical processes, representing
rich source of information for data driven medicine, engineering, and science.
The modeling of an image as a graph allows the application of graph-based
algorithms for content analysis. Amongst these, one of the most used is the
Dijkstra Single Source Shortest Path algorithm (DSSSP), which computes the path
with minimal cost from one starting node to all the other nodes of the graph.
However, the results of DSSSP remains unknown for nodes until they are
explored. Moreover, DSSSP execution is associated to frequent jumps between
distant locations in the graph, which results in non-optimal memory access,
reduced parallelization, and finally increased execution time. Therefore, we
propose AnyDijkstra, an iterative implementation of the Dijkstra SSSP algorithm
optimized for images, that retains anytime properties while accessing memory
following a cache-friendly scheme and maximizing parallelization.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 13:38:23 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Pizzagalli",
"Diego Ulisse",
""
],
[
"Krause",
"Rolf",
""
]
] |
new_dataset
| 0.993114 |
2210.15451
|
Diddigi Raghuram Bharadwaj
|
Diddigi Raghu Ram Bharadwaj, Lakshya Kumar, Saif Jawaid, Sreekanth
Vempati
|
Fine-Grained Session Recommendations in E-commerce using Deep
Reinforcement Learning
| null | null | null | null |
cs.IR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sustaining users' interest and keeping them engaged in the platform is very
important for the success of an e-commerce business. A session encompasses
different activities of a user between logging into the platform and logging
out or making a purchase. User activities in a session can be classified into
two groups: Known Intent and Unknown intent. Known intent activity pertains to
the session where the intent of a user to browse/purchase a specific product
can be easily captured. Whereas in unknown intent activity, the intent of the
user is not known. For example, consider the scenario where a user enters the
session to casually browse the products over the platform, similar to the
window shopping experience in the offline setting. While recommending similar
products is essential in the former, accurately understanding the intent and
recommending interesting products is essential in the latter setting in order
to retain a user. In this work, we focus primarily on the unknown intent
setting where our objective is to recommend a sequence of products to a user in
a session to sustain their interest, keep them engaged and possibly drive them
towards purchase. We formulate this problem in the framework of the Markov
Decision Process (MDP), a popular mathematical framework for sequential
decision making and solve it using Deep Reinforcement Learning (DRL)
techniques. However, training the next product recommendation is difficult in
the RL paradigm due to large variance in browse/purchase behavior of the users.
Therefore, we break the problem down into predicting various product
attributes, where a pattern/trend can be identified and exploited to build
accurate models. We show that the DRL agent provides better performance
compared to a greedy strategy.
|
[
{
"version": "v1",
"created": "Thu, 20 Oct 2022 13:22:13 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Bharadwaj",
"Diddigi Raghu Ram",
""
],
[
"Kumar",
"Lakshya",
""
],
[
"Jawaid",
"Saif",
""
],
[
"Vempati",
"Sreekanth",
""
]
] |
new_dataset
| 0.998384 |
2210.15478
|
Vittorio Lippi
|
Vittorio Lippi and Christoph Maurer and Thomas Mergner
|
Human-Likeness Indicator for Robot Posture Control and Balance
|
16 pages, 5 Figures. arXiv admin note: substantial text overlap with
arXiv:2110.14395
|
In Robotics, Computer Vision and Intelligent Systems Vol. 1667,
Ser. CCIS, pp. 1-16. Springer (2022)
|
10.1007/978-3-031-19650-8_5
| null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Similarly to humans, humanoid robots require posture control and balance to
walk and interact with the environment. In this work posture control in
perturbed conditions is evaluated as a performance test for humanoid control. A
specific performance indicator is proposed: the score is based on the
comparison between the body sway of the tested humanoid standing on a moving
surface and the sway produced by healthy subjects performing the same
experiment. This approach is here oriented to the evaluation of a
human-likeness. The measure is tested using a humanoid robot in order to
demonstrate a typical usage of the proposed evaluation scheme and an example of
how to improve robot control on the basis of such a performance indicator score
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 14:23:16 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Lippi",
"Vittorio",
""
],
[
"Maurer",
"Christoph",
""
],
[
"Mergner",
"Thomas",
""
]
] |
new_dataset
| 0.964012 |
2210.15638
|
Gaurav Sahu
|
Olga Vechtomova, Gaurav Sahu
|
LyricJam Sonic: A Generative System for Real-Time Composition and
Musical Improvisation
|
15 pages, 9 figures, 2 tables
| null | null | null |
cs.SD cs.AI cs.CL cs.LG cs.MM eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Electronic music artists and sound designers have unique workflow practices
that necessitate specialized approaches for developing music information
retrieval and creativity support tools. Furthermore, electronic music
instruments, such as modular synthesizers, have near-infinite possibilities for
sound creation and can be combined to create unique and complex audio paths.
The process of discovering interesting sounds is often serendipitous and
impossible to replicate. For this reason, many musicians in electronic genres
record audio output at all times while they work in the studio. Subsequently,
it is difficult for artists to rediscover audio segments that might be suitable
for use in their compositions from thousands of hours of recordings. In this
paper, we describe LyricJam Sonic -- a novel creative tool for musicians to
rediscover their previous recordings, re-contextualize them with other
recordings, and create original live music compositions in real-time. A
bi-modal AI-driven approach uses generated lyric lines to find matching audio
clips from the artist's past studio recordings, and uses them to generate new
lyric lines, which in turn are used to find other clips, thus creating a
continuous and evolving stream of music and lyrics. The intent is to keep the
artists in a state of creative flow conducive to music creation rather than
taking them into an analytical/critical state of deliberately searching for
past audio segments. The system can run in either a fully autonomous mode
without user input, or in a live performance mode, where the artist plays live
music, while the system "listens" and creates a continuous stream of music and
lyrics in response.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 17:27:58 GMT"
}
] | 2022-10-28T00:00:00 |
[
[
"Vechtomova",
"Olga",
""
],
[
"Sahu",
"Gaurav",
""
]
] |
new_dataset
| 0.999739 |
2202.04947
|
Merey Ramazanova
|
Merey Ramazanova, Victor Escorcia, Fabian Caba Heilbron, Chen Zhao,
Bernard Ghanem
|
OWL (Observe, Watch, Listen): Audiovisual Temporal Context for
Localizing Actions in Egocentric Videos
| null | null | null | null |
cs.CV cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Egocentric videos capture sequences of human activities from a first-person
perspective and can provide rich multimodal signals. However, most current
localization methods use third-person videos and only incorporate visual
information. In this work, we take a deep look into the effectiveness of
audiovisual context in detecting actions in egocentric videos and introduce a
simple-yet-effective approach via Observing, Watching, and Listening (OWL). OWL
leverages audiovisual information and context for egocentric temporal action
localization (TAL). We validate our approach in two large-scale datasets,
EPIC-Kitchens, and HOMAGE. Extensive experiments demonstrate the relevance of
the audiovisual temporal context. Namely, we boost the localization performance
(mAP) over visual-only models by +2.23% and +3.35% in the above datasets.
|
[
{
"version": "v1",
"created": "Thu, 10 Feb 2022 10:50:52 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Feb 2022 15:30:49 GMT"
},
{
"version": "v3",
"created": "Wed, 26 Oct 2022 13:24:39 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Ramazanova",
"Merey",
""
],
[
"Escorcia",
"Victor",
""
],
[
"Heilbron",
"Fabian Caba",
""
],
[
"Zhao",
"Chen",
""
],
[
"Ghanem",
"Bernard",
""
]
] |
new_dataset
| 0.958125 |
2203.08480
|
Jiangjie Chen
|
Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang,
Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou
|
E-KAR: A Benchmark for Rationalizing Natural Language Analogical
Reasoning
|
Accepted to ACL 2022 (Findings)
| null |
10.18653/v1/2022.findings-acl.311
| null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability to recognize analogies is fundamental to human cognition.
Existing benchmarks to test word analogy do not reveal the underneath process
of analogical reasoning of neural models. Holding the belief that models
capable of reasoning should be right for the right reasons, we propose a
first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning
benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in
English) problems sourced from the Civil Service Exams, which require intensive
background knowledge to solve. More importantly, we design a free-text
explanation scheme to explain whether an analogy should be drawn, and manually
annotate them for each and every question and candidate answer. Empirical
results suggest that this benchmark is very challenging for some
state-of-the-art models for both explanation generation and analogical question
answering tasks, which invites further research in this area.
|
[
{
"version": "v1",
"created": "Wed, 16 Mar 2022 09:16:38 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Chen",
"Jiangjie",
""
],
[
"Xu",
"Rui",
""
],
[
"Fu",
"Ziquan",
""
],
[
"Shi",
"Wei",
""
],
[
"Li",
"Zhongqiao",
""
],
[
"Zhang",
"Xinbo",
""
],
[
"Sun",
"Changzhi",
""
],
[
"Li",
"Lei",
""
],
[
"Xiao",
"Yanghua",
""
],
[
"Zhou",
"Hao",
""
]
] |
new_dataset
| 0.999451 |
2205.12697
|
Haoyu Dong
|
Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han, Dongmei Zhang
|
PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation
|
EMNLP'22
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Logical table-to-text generation is a task that involves generating logically
faithful sentences from tables, which requires models to derive logical level
facts from table records via logical inference. It raises a new challenge on
the logical-level content planning of table-to-text models. However, directly
learning the logical inference knowledge from table-text pairs is very
difficult for neural models because of the ambiguity of natural language and
the scarcity of parallel data. Hence even large-scale pre-trained language
models present low logical fidelity on logical table-to-text. In this work, we
propose a PLOG (Pretrained Logical Form Generator) framework to improve the
generation fidelity. Specifically, PLOG is first pretrained on a
table-to-logic-form generation (table-to-logic) task, then finetuned on
downstream table-to-text tasks. The formal definition of logical forms enables
us to collect large amount of accurate logical forms from tables without human
annotation. In addition, PLOG can learn logical inference from table-logic
pairs much more definitely than from table-text pairs. To evaluate our model,
we further collect a controlled logical table-to-text dataset CONTLOG based on
an existing dataset. On two benchmarks, LOGICNLG and CONTLOG, PLOG outperforms
strong baselines by a large margin on the logical fidelity, demonstrating the
effectiveness of table-to-logic pretraining.
|
[
{
"version": "v1",
"created": "Wed, 25 May 2022 11:55:54 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Oct 2022 02:00:54 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Liu",
"Ao",
""
],
[
"Dong",
"Haoyu",
""
],
[
"Okazaki",
"Naoaki",
""
],
[
"Han",
"Shi",
""
],
[
"Zhang",
"Dongmei",
""
]
] |
new_dataset
| 0.997159 |
2207.11155
|
Domenico Fabio Savo
|
Piero Bonatti, Gianluca Cima, Domenico Lembo, Lorenzo Marconi,
Riccardo Rosati, Luigi Sauro, Domenico Fabio Savo
|
CQE in OWL 2 QL: A "Longest Honeymoon" Approach (extended version)
|
This paper is the extended version of "P.Bonatti, G.Cima, D.Lembo,
L.Marconi, R.Rosati, L.Sauro, and D.F.Savo. Controlled query evaluation in
OWL 2 QL: A "Longest Honeymoon" approach" accepted for publication at ISWC
2022
| null |
10.1007/978-3-031-19433-7_25
| null |
cs.DB cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Controlled Query Evaluation (CQE) has been recently studied in the context of
Semantic Web ontologies. The goal of CQE is concealing some query answers so as
to prevent external users from inferring confidential information. In general,
there exist multiple, mutually incomparable ways of concealing answers, and
previous CQE approaches choose in advance which answers are visible and which
are not. In this paper, instead, we study a dynamic CQE method, namely, we
propose to alter the answer to the current query based on the evaluation of
previous ones. We aim at a system that, besides being able to protect
confidential data, is maximally cooperative, which intuitively means that it
answers affirmatively to as many queries as possible; it achieves this goal by
delaying answer modifications as much as possible. We also show that the
behavior we get cannot be intensionally simulated through a static approach,
independent of query history. Interestingly, for OWL 2 QL ontologies and policy
expressed through denials, query evaluation under our semantics is first-order
rewritable, and thus in AC0 in data complexity. This paves the way for the
development of practical algorithms, which we also preliminarily discuss in the
paper.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 15:51:15 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Bonatti",
"Piero",
""
],
[
"Cima",
"Gianluca",
""
],
[
"Lembo",
"Domenico",
""
],
[
"Marconi",
"Lorenzo",
""
],
[
"Rosati",
"Riccardo",
""
],
[
"Sauro",
"Luigi",
""
],
[
"Savo",
"Domenico Fabio",
""
]
] |
new_dataset
| 0.983996 |
2210.09706
|
Wasja Brunotte
|
Wasja Brunotte, Alexander Specht, Larissa Chazette, Kurt Schneider
|
Privacy Explanations - A Means to End-User Trust
| null | null | null | null |
cs.SE cs.CY cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Software systems are ubiquitous, and their use is ingrained in our everyday
lives. They enable us to get in touch with people quickly and easily, support
us in gathering information, and help us perform our daily tasks. In return, we
provide these systems with a large amount of personal information, often
unaware that this is jeopardizing our privacy. End users are typically unaware
of what data is collected, for what purpose, who has access to it, and where
and how it is stored. To address this issue, we looked into how explainability
might help to tackle this problem. We created privacy explanations that aim to
help to clarify to end users why and for what purposes specific data is
required. We asked end users about privacy explanations in a survey and found
that the majority of respondents (91.6 \%) are generally interested in
receiving privacy explanations. Our findings reveal that privacy explanations
can be an important step towards increasing trust in software systems and can
increase the privacy awareness of end users. These findings are a significant
step in developing privacy-aware systems and incorporating usable privacy
features into them, assisting users in protecting their privacy.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 09:30:37 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Oct 2022 06:35:08 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Brunotte",
"Wasja",
""
],
[
"Specht",
"Alexander",
""
],
[
"Chazette",
"Larissa",
""
],
[
"Schneider",
"Kurt",
""
]
] |
new_dataset
| 0.96246 |
2210.10983
|
Zhicong Huang
|
Zhicong Huang, Jingwen Zhao, Zhijie Zheng, Dihu Chena, Haifeng Hu
|
PSA-Det3D: Pillar Set Abstraction for 3D object Detection
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Small object detection for 3D point cloud is a challenging problem because of
two limitations: (1) Perceiving small objects is much more diffcult than normal
objects due to the lack of valid points. (2) Small objects are easily blocked
which breaks the shape of their meshes in 3D point cloud. In this paper, we
propose a pillar set abstraction (PSA) and foreground point compensation (FPC)
and design a point-based detection network, PSA-Det3D, to improve the detection
performance for small object. The PSA embeds a pillar query operation on the
basis of set abstraction (SA) to expand its receptive field of the network,
which can aggregate point-wise features effectively. To locate more occluded
objects, we persent a proposal generation layer consisting of a foreground
point segmentation and a FPC module. Both the foreground points and the
estimated centers are finally fused together to generate the detection result.
The experiments on the KITTI 3D detection benchmark show that our proposed
PSA-Det3D outperforms other algorithms with high accuracy for small object
detection.
|
[
{
"version": "v1",
"created": "Thu, 20 Oct 2022 03:05:34 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Oct 2022 09:36:39 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Huang",
"Zhicong",
""
],
[
"Zhao",
"Jingwen",
""
],
[
"Zheng",
"Zhijie",
""
],
[
"Chena",
"Dihu",
""
],
[
"Hu",
"Haifeng",
""
]
] |
new_dataset
| 0.999419 |
2210.12467
|
Rajdeep Mukherjee
|
Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma,
Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy
Ganguly, Saptarshi Ghosh, Pawan Goyal
|
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long
Earnings Call Transcripts
|
14 pages; Accepted as a Long Paper in EMNLP 2022 (Main Conference);
Codes: https://github.com/rajdeep345/ECTSum
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite tremendous progress in automatic summarization, state-of-the-art
methods are predominantly trained to excel in summarizing short newswire
articles, or documents with strong layout biases such as scientific articles or
government reports. Efficient techniques to summarize financial documents,
including facts and figures, have largely been unexplored, majorly due to the
unavailability of suitable datasets. In this work, we present ECTSum, a new
dataset with transcripts of earnings calls (ECTs), hosted by publicly traded
companies, as documents, and short experts-written telegram-style bullet point
summaries derived from corresponding Reuters articles. ECTs are long
unstructured documents without any prescribed length limit or format. We
benchmark our dataset with state-of-the-art summarizers across various metrics
evaluating the content quality and factual consistency of the generated
summaries. Finally, we present a simple-yet-effective approach, ECT-BPS, to
generate a set of bullet points that precisely capture the important facts
discussed in the calls.
|
[
{
"version": "v1",
"created": "Sat, 22 Oct 2022 15:02:41 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Oct 2022 16:21:37 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Mukherjee",
"Rajdeep",
""
],
[
"Bohra",
"Abhinav",
""
],
[
"Banerjee",
"Akash",
""
],
[
"Sharma",
"Soumya",
""
],
[
"Hegde",
"Manjunath",
""
],
[
"Shaikh",
"Afreen",
""
],
[
"Shrivastava",
"Shivani",
""
],
[
"Dasgupta",
"Koustuv",
""
],
[
"Ganguly",
"Niloy",
""
],
[
"Ghosh",
"Saptarshi",
""
],
[
"Goyal",
"Pawan",
""
]
] |
new_dataset
| 0.999699 |
2210.14056
|
Ajay Chawda
|
Ajay Chawda, Stefanie Grimm, Marius Kloft
|
Unsupervised Anomaly Detection for Auditing Data and Impact of
Categorical Encodings
|
This work has been accepted at Proceedings of the Neurips 2022
Workshop on Synthetic Data 4ML
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we introduce the Vehicle Claims dataset, consisting of
fraudulent insurance claims for automotive repairs. The data belongs to the
more broad category of Auditing data, which includes also Journals and Network
Intrusion data. Insurance claim data are distinctively different from other
auditing data (such as network intrusion data) in their high number of
categorical attributes. We tackle the common problem of missing benchmark
datasets for anomaly detection: datasets are mostly confidential, and the
public tabular datasets do not contain relevant and sufficient categorical
attributes. Therefore, a large-sized dataset is created for this purpose and
referred to as Vehicle Claims (VC) dataset. The dataset is evaluated on shallow
and deep learning methods. Due to the introduction of categorical attributes,
we encounter the challenge of encoding them for the large dataset. As One Hot
encoding of high cardinal dataset invokes the "curse of dimensionality", we
experiment with GEL encoding and embedding layer for representing categorical
attributes. Our work compares competitive learning, reconstruction-error,
density estimation and contrastive learning approaches for Label, One Hot, GEL
encoding and embedding layer to handle categorical values.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 14:33:17 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Oct 2022 04:03:43 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Chawda",
"Ajay",
""
],
[
"Grimm",
"Stefanie",
""
],
[
"Kloft",
"Marius",
""
]
] |
new_dataset
| 0.951189 |
2210.14252
|
Dawei Liang
|
Dawei Liang, Hang Su, Tarun Singh, Jay Mahadeokar, Shanil Puri, Jiedan
Zhu, Edison Thomaz, Mike Seltzer
|
Dynamic Speech Endpoint Detection with Regression Targets
|
Manuscript submitted to ICASSP 2023
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Interactive voice assistants have been widely used as input interfaces in
various scenarios, e.g. on smart homes devices, wearables and on AR devices.
Detecting the end of a speech query, i.e. speech end-pointing, is an important
task for voice assistants to interact with users. Traditionally, speech
end-pointing is based on pure classification methods along with arbitrary
binary targets. In this paper, we propose a novel regression-based speech
end-pointing model, which enables an end-pointer to adjust its detection
behavior based on context of user queries. Specifically, we present a pause
modeling method and show its effectiveness for dynamic end-pointing. Based on
our experiments with vendor-collected smartphone and wearables speech queries,
our strategy shows a better trade-off between endpointing latency and accuracy,
compared to the traditional classification-based method. We further discuss the
benefits of this model and generalization of the framework in the paper.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 18:09:42 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Liang",
"Dawei",
""
],
[
"Su",
"Hang",
""
],
[
"Singh",
"Tarun",
""
],
[
"Mahadeokar",
"Jay",
""
],
[
"Puri",
"Shanil",
""
],
[
"Zhu",
"Jiedan",
""
],
[
"Thomaz",
"Edison",
""
],
[
"Seltzer",
"Mike",
""
]
] |
new_dataset
| 0.990497 |
2210.14299
|
Hanzi Xu
|
Hanzi Xu, Slobodan Vucetic, Wenpeng Yin
|
OpenStance: Real-world Zero-shot Stance Detection
|
CoNLL 2022 Camera-ready version
| null | null | null |
cs.CL
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Prior studies of zero-shot stance detection identify the attitude of texts
towards unseen topics occurring in the same document corpus. Such task
formulation has three limitations: (i) Single domain/dataset. A system is
optimized on a particular dataset from a single domain; therefore, the
resulting system cannot work well on other datasets; (ii) the model is
evaluated on a limited number of unseen topics; (iii) it is assumed that part
of the topics has rich annotations, which might be impossible in real-world
applications. These drawbacks will lead to an impractical stance detection
system that fails to generalize to open domains and open-form topics. This work
defines OpenStance: open-domain zero-shot stance detection, aiming to handle
stance detection in an open world with neither domain constraints nor
topic-specific annotations. The key challenge of OpenStance lies in the
open-domain generalization: learning a system with fully unspecific supervision
but capable of generalizing to any dataset. To solve OpenStance, we propose to
combine indirect supervision, from textual entailment datasets, and weak
supervision, from data generated automatically by pre-trained Language Models.
Our single system, without any topic-specific supervision, outperforms the
supervised method on three popular datasets. To our knowledge, this is the
first work that studies stance detection under the open-domain zero-shot
setting. All data and code are publicly released.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 19:50:36 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Xu",
"Hanzi",
""
],
[
"Vucetic",
"Slobodan",
""
],
[
"Yin",
"Wenpeng",
""
]
] |
new_dataset
| 0.999298 |
2210.14349
|
Menghe Zhang
|
Menghe Zhang, Weichen Liu, Nadir Weibel, Jurgen Schulze
|
A DirectX-Based DICOM Viewer for Multi-User Surgical Planning in
Augmented Reality
| null |
ISVC 2022 symposium proceeding, will be on Lecture Notes in
Computer Science (LNCS) series
| null | null |
cs.MM cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Preoperative medical imaging is an essential part of surgical planning. The
data from medical imaging devices, such as CT and MRI scanners, consist of
stacks of 2D images in DICOM format. Conversely, advances in 3D data
visualization provide further information by assembling cross-sections into 3D
volumetric datasets. As Microsoft unveiled the HoloLens 2 (HL2), which is
considered one of the best Mixed Reality (XR) headsets in the market, it
promised to enhance visualization in 3D by providing an immersive experience to
users. This paper introduces a prototype holographic XR DICOM Viewer for the 3D
visualization of DICOM image sets on HL2 for surgical planning. We first
developed a standalone graphical C++ engine using the native DirectX11 API and
HLSL shaders. Based on that, the prototype further applies the OpenXR API for
potential deployment on a wide range of devices from vendors across the XR
spectrum. With native access to the device, our prototype unravels the
limitation of hardware capabilities on HL2 for 3D volume rendering and
interaction. Moreover, smartphones can act as input devices to provide another
user interaction method by connecting to our server. In this paper, we present
a holographic DICOM viewer for the HoloLens 2 and contribute (i) a prototype
that renders the DICOM image stacks in real-time on HL2, (ii) three types of
user interactions in XR, and (iii) a preliminary qualitative evaluation of our
prototype.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 21:22:00 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Zhang",
"Menghe",
""
],
[
"Liu",
"Weichen",
""
],
[
"Weibel",
"Nadir",
""
],
[
"Schulze",
"Jurgen",
""
]
] |
new_dataset
| 0.999099 |
2210.14363
|
Kishaloy Halder
|
Kishaloy Halder, Josip Krapac, Dmitry Goryunov, Anthony Brew, Matti
Lyra, Alsida Dizdari, William Gillett, Adrien Renahy, Sinan Tang
|
Enhancing Product Safety in E-Commerce with NLP
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Ensuring safety of the products offered to the customers is of paramount
importance to any e- commerce platform. Despite stringent quality and safety
checking of products listed on these platforms, occasionally customers might
receive a product that can pose a safety issue arising out of its use. In this
paper, we present an innovative mechanism of how a large scale multinational
e-commerce platform, Zalando, uses Natural Language Processing techniques to
assist timely investigation of the potentially unsafe products mined directly
from customer written claims in unstructured plain text. We systematically
describe the types of safety issues that concern Zalando customers. We
demonstrate how we map this core business problem into a supervised text
classification problem with highly imbalanced, noisy, multilingual data in a
AI-in-the-loop setup with a focus on Key Performance Indicator (KPI) driven
evaluation. Finally, we present detailed ablation studies to show a
comprehensive comparison between different classification techniques. We
conclude the work with how this NLP model was deployed.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 22:10:30 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Halder",
"Kishaloy",
""
],
[
"Krapac",
"Josip",
""
],
[
"Goryunov",
"Dmitry",
""
],
[
"Brew",
"Anthony",
""
],
[
"Lyra",
"Matti",
""
],
[
"Dizdari",
"Alsida",
""
],
[
"Gillett",
"William",
""
],
[
"Renahy",
"Adrien",
""
],
[
"Tang",
"Sinan",
""
]
] |
new_dataset
| 0.98927 |
2210.14373
|
Levent Guvenc
|
Karina Meneses-Cime, Bilin Aksun-Guvenc, Levent Guvenc
|
Shared Autonomous Vehicle Mobility for a Transportation Underserved City
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper proposes the use of an on-demand, ride hailed and ride-Shared
Autonomous Vehicle (SAV) service as a feasible solution to serve the mobility
needs of a small city where fixed route, circulator type public transportation
may be too expensive to operate. The presented work builds upon our earlier
work that modeled the city of Marysville, Ohio as an example of such a city,
with realistic traffic behavior, and trip requests. A simple SAV dispatcher is
implemented to model the behavior of the proposed on-demand mobility service.
The goal of the service is to optimally distribute SAVs along the network to
allocate passengers and shared rides. The pickup and drop-off locations are
strategically placed along the network to provide mobility from affordable
housing, which are also transit deserts, to locations corresponding to jobs and
other opportunities. The study is carried out by varying the behaviors of the
SAV driving system from cautious to aggressive along with the size of the SAV
fleet and analyzing their corresponding performance. It is found that the size
of the network and behavior of AV driving system behavior results in an optimal
number of SAVs after which increasing the number of SAVs does not improve
overall mobility. For the Marysville network, which is a 9 mile by 8 mile
network, this happens at the mark of a fleet of 8 deployed SAVs. The results
show that the introduction of the proposed SAV service with a simple optimal
shared scheme can provide access to services and jobs to hundreds of people in
a small sized city.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 22:44:59 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Meneses-Cime",
"Karina",
""
],
[
"Aksun-Guvenc",
"Bilin",
""
],
[
"Guvenc",
"Levent",
""
]
] |
new_dataset
| 0.992244 |
2210.14395
|
Seungwhan Moon
|
Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Alireza Dirafzoon,
Aparajita Saraf, Amy Bearman, Babak Damavandi
|
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from
Egocentric Videos and Text
| null | null | null | null |
cs.CV cs.CL cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present IMU2CLIP, a novel pre-training approach to align Inertial
Measurement Unit (IMU) motion sensor recordings with video and text, by
projecting them into the joint representation space of Contrastive
Language-Image Pre-training (CLIP). The proposed approach allows IMU2CLIP to
translate human motions (as measured by IMU sensors) into their corresponding
textual descriptions and videos -- while preserving the transitivity across
these modalities.
We explore several new IMU-based applications that IMU2CLIP enables, such as
motion-based media retrieval and natural language reasoning tasks with motion
data. In addition, we show that IMU2CLIP can significantly improve the
downstream performance when fine-tuned for each application (e.g. activity
recognition), demonstrating the universal usage of IMU2CLIP as a new
pre-trained resource. Our code will be made publicly available.
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 00:22:41 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Moon",
"Seungwhan",
""
],
[
"Madotto",
"Andrea",
""
],
[
"Lin",
"Zhaojiang",
""
],
[
"Dirafzoon",
"Alireza",
""
],
[
"Saraf",
"Aparajita",
""
],
[
"Bearman",
"Amy",
""
],
[
"Damavandi",
"Babak",
""
]
] |
new_dataset
| 0.999587 |
2210.14408
|
Puyang Zhao
|
Puyang Zhao, Wei Tian, Lefu Xiao, Xinhui Liu, Jingjin Wu
|
An Attention-based Long Short-Term Memory Framework for Detection of
Bitcoin Scams
| null | null | null | null |
cs.CR cs.CY cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bitcoin is the most common cryptocurrency involved in cyber scams.
Cybercriminals often utilize pseudonymity and privacy protection mechanism
associated with Bitcoin transactions to make their scams virtually untraceable.
The Ponzi scheme has attracted particularly significant attention among Bitcoin
fraudulent activities. This paper considers a multi-class classification
problem to determine whether a transaction is involved in Ponzi schemes or
other cyber scams, or is a non-scam transaction. We design a specifically
designed crawler to collect data and propose a novel Attention-based Long
Short-Term Memory (A-LSTM) method for the classification problem. The
experimental results show that the proposed model has better efficiency and
accuracy than existing approaches, including Random Forest, Extra Trees,
Gradient Boosting, and classical LSTM. With correctly identified scam features,
our proposed A-LSTM achieves an F1-score over 82% for the original data and
outperforms the existing approaches.
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 01:20:21 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Zhao",
"Puyang",
""
],
[
"Tian",
"Wei",
""
],
[
"Xiao",
"Lefu",
""
],
[
"Liu",
"Xinhui",
""
],
[
"Wu",
"Jingjin",
""
]
] |
new_dataset
| 0.995856 |
2210.14424
|
Mukund Rungta
|
Mukund Rungta, Janvijay Singh, Saif M. Mohammad and Diyi Yang
|
Geographic Citation Gaps in NLP Research
|
EMNLP 2022 Main Conference
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In a fair world, people have equitable opportunities to education, to conduct
scientific research, to publish, and to get credit for their work, regardless
of where they live. However, it is common knowledge among researchers that a
vast number of papers accepted at top NLP venues come from a handful of western
countries and (lately) China; whereas, very few papers from Africa and South
America get published. Similar disparities are also believed to exist for paper
citation counts. In the spirit of "what we do not measure, we cannot improve",
this work asks a series of questions on the relationship between geographical
location and publication success (acceptance in top NLP venues and citation
impact). We first created a dataset of 70,000 papers from the ACL Anthology,
extracted their meta-information, and generated their citation network. We then
show that not only are there substantial geographical disparities in paper
acceptance and citation but also that these disparities persist even when
controlling for a number of variables such as venue of publication and
sub-field of NLP. Further, despite some steps taken by the NLP community to
improve geographical diversity, we show that the disparity in publication
metrics across locations is still on an increasing trend since the early 2000s.
We release our code and dataset here:
https://github.com/iamjanvijay/acl-cite-net
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 02:25:23 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Rungta",
"Mukund",
""
],
[
"Singh",
"Janvijay",
""
],
[
"Mohammad",
"Saif M.",
""
],
[
"Yang",
"Diyi",
""
]
] |
new_dataset
| 0.988034 |
2210.14472
|
Gihan Weeraprameshwara
|
Gihan Weeraprameshwara, Vihanga Jayawickrama, Nisansa de Silva,
Yudhanjaya Wijeratne
|
Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource
Languages
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In the process of numerically modeling natural languages, developing language
embeddings is a vital step. However, it is challenging to develop functional
embeddings for resource-poor languages such as Sinhala, for which sufficiently
large corpora, effective language parsers, and any other required resources are
difficult to find. In such conditions, the exploitation of existing models to
come up with an efficacious embedding methodology to numerically represent text
could be quite fruitful. This paper explores the effectivity of several
one-tiered and two-tiered embedding architectures in representing Sinhala text
in the sentiment analysis domain. With our findings, the two-tiered embedding
architecture where the lower-tier consists of a word embedding and the
upper-tier consists of a sentence embedding has been proven to perform better
than one-tier word embeddings, by achieving a maximum F1 score of 88.04% in
contrast to the 83.76% achieved by word embedding models. Furthermore,
embeddings in the hyperbolic space are also developed and compared with
Euclidean embeddings in terms of performance. A sentiment data set consisting
of Facebook posts and associated reactions have been used for this research. To
effectively compare the performance of different embedding systems, the same
deep neural network structure has been trained on sentiment data with each of
the embedding systems used to encode the text associated.
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 04:46:23 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Weeraprameshwara",
"Gihan",
""
],
[
"Jayawickrama",
"Vihanga",
""
],
[
"de Silva",
"Nisansa",
""
],
[
"Wijeratne",
"Yudhanjaya",
""
]
] |
new_dataset
| 0.965605 |
2210.14494
|
Changyoon Lee
|
Changyoon Lee, Yeon Seonwoo, Alice Oh
|
CS1QA: A Dataset for Assisting Code-based Question Answering in an
Introductory Programming Course
| null | null |
10.18653/v1/2022.naacl-main.148
| null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We introduce CS1QA, a dataset for code-based question answering in the
programming education domain. CS1QA consists of 9,237 question-answer pairs
gathered from chat logs in an introductory programming class using Python, and
17,698 unannotated chat data with code. Each question is accompanied with the
student's code, and the portion of the code relevant to answering the question.
We carefully design the annotation process to construct CS1QA, and analyze the
collected dataset in detail. The tasks for CS1QA are to predict the question
type, the relevant code snippet given the question and the code and retrieving
an answer from the annotated corpus. Results for the experiments on several
baseline models are reported and thoroughly analyzed. The tasks for CS1QA
challenge models to understand both the code and natural language. This unique
dataset can be used as a benchmark for source code comprehension and question
answering in the educational setting.
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 05:40:34 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Lee",
"Changyoon",
""
],
[
"Seonwoo",
"Yeon",
""
],
[
"Oh",
"Alice",
""
]
] |
new_dataset
| 0.999706 |
2210.14505
|
Xiujing Zheng
|
Xiujing Zheng
|
Constructions of entanglement-assisted quantum MDS from generalized
Reed-Solomon codes
|
17 pages. 6 table
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Entanglement-assisted quantum error-correcting (EAQEC) codes are a
generalization of standard stabilizer quantum error-correcting codes, which can
be possibly constructed from any classical codes by relaxing self-orthogonal
condition with the help of pre-shared entanglement between the sender and the
receiver. In this paper, by using generalized Reed-Solomon codes, we construct
two families of entanglement-assisted quantum error-correcting MDS (EAQMDS)
codes with parameters $[[\frac{b({q^2}-1)}{a}+\frac{{q^2} - 1}{a},
\frac{b({q^2}-1)}{a}+\frac{{q^2}-1}{a}-2d+c+2,d;c]]_q$, where $q$ is a prime
power and $a| (q+1)$. Among our constructions, the EAQMDS codes have much
larger minimum distance than the known EAQMDS codes with the same length and
consume the same number of ebits. Moreover, some of the lengths of ours EAQMDS
codes may not be divisors of $q^2\pm 1$, which are new and different from all
the previously known ones.
|
[
{
"version": "v1",
"created": "Wed, 26 Oct 2022 06:30:15 GMT"
}
] | 2022-10-27T00:00:00 |
[
[
"Zheng",
"Xiujing",
""
]
] |
new_dataset
| 0.961563 |
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